The most complete open-source tool for Twitter intelligence analysis

Twitter Intelligence OSINT project performs tracking and analysis of the Twitter

X Intelligence CLI — search, monitor, analyze, and engage on X/Twitter. TypeScript + Bun. AI agent skill. Using xAPI & xAI api keys

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Twitter geo intelligence tool. Generates a Leaflet map for a given user or from an existing collection of tweets.

Automated Twitter bots, run by the artificial artificial intelligence of Amazon Mechanical Turk.

​ ​#​ ​Ukraine-Cyber-Operations ​Curated Intelligence is working with analysts from around the world to provide useful information to organisations in Ukraine looking for additional free threat intelligence. Slava Ukraini. Glory to Ukraine. ([​Blog​](https://www.curatedintel.org/2021/08/welcome.html) | [​Twitter​](https://twitter.com/CuratedIntel) | [​LinkedIn​](https://www.linkedin.com/company/curatedintelligence/)) ​ ​ ​###​ ​Analyst Comments: ​-​ 2022-02-25 ​  ​-​ Creation of the initial repository to help organisations in Ukraine ​  ​-​ Added [​Threat Reports​](https://github.com/curated-intel/Ukraine-Cyber-Operations#threat-reports) section ​  ​-​ Added [​Vendor Support​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vendor-support) section ​-​ 2022-02-26 ​  ​-​ Additional resources, chronologically ordered (h/t Orange-CD) ​  ​-​ Added [​Vetted OSINT Sources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vetted-osint-sources) section  ​  ​-​ Added [​Miscellaneous Resources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#miscellaneous-resources) section ​-​ 2022-02-27 ​  ​-​ Additional threat reports have been added ​  ​-​ Added [​Data Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#data-brokers) section ​  ​-​ Added [​Access Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#access-brokers) section ​-​ 2022-02-28 ​  ​-​ Added Russian Cyber Operations Against Ukraine Timeline by ETAC ​  ​-​ Added Vetted and Contextualized [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv) by ETAC ​-​ 2022-03-01 ​  ​-​ Additional threat reports and resources have been added ​-​ 2022-03-02 ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2011) have been added ​  ​-​ Added vetted [​YARA rule collection​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/yara) from the Threat Reports by ETAC ​  ​-​ Added loosely-vetted [​IOC Threat Hunt Feeds​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/KPMG-Egyde_Ukraine-Crisis_Feeds/MISP-CSV_MediumConfidence_Filtered) by KPMG-Egyde CTI (h/t [​0xDISREL​](https://twitter.com/0xDISREL)) ​    ​-​ IOCs shared by these feeds are ​`LOW-TO-MEDIUM CONFIDENCE`​ we strongly recommend NOT adding them to a blocklist ​    ​-​ These could potentially be used for ​`THREAT HUNTING`​ and could be added to a ​`WATCHLIST` ​    ​-​ IOCs are generated in ​`MISP COMPATIBLE`​ CSV format ​-​ 2022-03-03 ​  ​-​ Additional threat reports and vendor support resources have been added ​  ​-​ Updated [​Log4Shell IOC Threat Hunt Feeds​](https://github.com/curated-intel/Log4Shell-IOCs/tree/main/KPMG_Log4Shell_Feeds) by KPMG-Egyde CTI; not directly related to Ukraine, but still a widespread vulnerability. ​  ​-​ Added diagram of Russia-Ukraine Cyberwar Participants 2022 by ETAC ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2042) have been added ​####​ ​`Threat Reports` ​| Date | Source | Threat(s) | URL | ​| --- | --- | --- | --- | ​| 14 JAN | SSU Ukraine | Website Defacements | [ssu.gov.ua](https://ssu.gov.ua/novyny/sbu-rozsliduie-prychetnist-rosiiskykh-spetssluzhb-do-sohodnishnoi-kiberataky-na-orhany-derzhavnoi-vlady-ukrainy)| ​| 15 JAN | Microsoft | WhisperGate wiper (DEV-0586) | [microsoft.com](https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/) | ​| 19 JAN | Elastic | WhisperGate wiper (Operation BleedingBear) | [elastic.github.io](https://elastic.github.io/security-research/malware/2022/01/01.operation-bleeding-bear/article/) | ​| 31 JAN | Symantec | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/shuckworm-gamaredon-espionage-ukraine) | ​| 2 FEB | RaidForums | Access broker "GodLevel" offering Ukrainain algricultural exchange | RaidForums [not linked] | ​| 2 FEB | CERT-UA | UAC-0056 using SaintBot and OutSteel malware | [cert.gov.ua](https://cert.gov.ua/article/18419) | ​| 3 FEB | PAN Unit42 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [unit42.paloaltonetworks.com](https://unit42.paloaltonetworks.com/gamaredon-primitive-bear-ukraine-update-2021/) | ​| 4 FEB | Microsoft | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [microsoft.com](https://www.microsoft.com/security/blog/2022/02/04/actinium-targets-ukrainian-organizations/) | ​| 8 FEB | NSFOCUS | Lorec53 (aka UAC-0056, EmberBear, BleedingBear) | [nsfocusglobal.com](https://nsfocusglobal.com/apt-retrospection-lorec53-an-active-russian-hack-group-launched-phishing-attacks-against-georgian-government) | ​| 15 FEB | CERT-UA | DDoS attacks against the name server of government websites as well as Oschadbank (State Savings Bank) & Privatbank (largest commercial bank). False SMS and e-mails to create panic | [cert.gov.ua](https://cert.gov.ua/article/37139) | ​| 23 FEB | The Daily Beast | Ukrainian troops receive threatening SMS messages | [thedailybeast.com](https://www.thedailybeast.com/cyberattacks-hit-websites-and-psy-ops-sms-messages-targeting-ukrainians-ramp-up-as-russia-moves-into-ukraine) | ​| 23 FEB | UK NCSC | Sandworm/VoodooBear (GRU) | [ncsc.gov.uk](https://www.ncsc.gov.uk/files/Joint-Sandworm-Advisory.pdf) | ​| 23 FEB | SentinelLabs | HermeticWiper | [sentinelone.com]( https://www.sentinelone.com/labs/hermetic-wiper-ukraine-under-attack/ ) | ​| 24 FEB | ESET | HermeticWiper | [welivesecurity.com](https://www.welivesecurity.com/2022/02/24/hermeticwiper-new-data-wiping-malware-hits-ukraine/) | ​| 24 FEB | Symantec | HermeticWiper, PartyTicket ransomware, CVE-2021-1636, unknown webshell | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/ukraine-wiper-malware-russia) | ​| 24 FEB | Cisco Talos | HermeticWiper | [blog.talosintelligence.com](https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html) | ​| 24 FEB | Zscaler | HermeticWiper | [zscaler.com](https://www.zscaler.com/blogs/security-research/hermetic-wiper-resurgence-targeted-attacks-ukraine) | ​| 24 FEB | Cluster25 | HermeticWiper | [cluster25.io](https://cluster25.io/2022/02/24/ukraine-analysis-of-the-new-disk-wiping-malware/) | ​| 24 FEB | CronUp | Data broker "FreeCivilian" offering multiple .gov.ua | [twitter.com/1ZRR4H](https://twitter.com/1ZRR4H/status/1496931721052311557)| ​| 24 FEB | RaidForums | Data broker "Featherine" offering diia.gov.ua | RaidForums [not linked] | ​| 24 FEB | DomainTools | Unknown scammers | [twitter.com/SecuritySnacks](https://twitter.com/SecuritySnacks/status/1496956492636905473?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497339266329894920?s=20&t=opOtwpn82ztiFtwUbLkm9Q) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497208285472215042)| ​| 25 FEB | Microsoft | HermeticWiper | [gist.github.com](https://gist.github.com/fr0gger/7882fde2b1b271f9e886a4a9b6fb6b7f) | ​| 25 FEB | 360 NetLab | DDoS (Mirai, Gafgyt, IRCbot, Ripprbot, Moobot) | [blog.netlab.360.com](https://blog.netlab.360.com/some_details_of_the_ddos_attacks_targeting_ukraine_and_russia_in_recent_days/) | ​| 25 FEB | Conti [themselves] | Conti ransomware, BazarLoader | Conti News .onion [not linked] | ​| 25 FEB | CoomingProject [themselves] | Data Hostage Group | CoomingProject Telegram [not linked] | ​| 25 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT-UA Facebook](https://facebook.com/story.php?story_fbid=312939130865352&id=100064478028712)| ​| 25 FEB | Sekoia | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/sekoia_io](https://twitter.com/sekoia_io/status/1497239319295279106) | ​| 25 FEB | @jaimeblascob | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/jaimeblasco](https://twitter.com/jaimeblascob/status/1497242668627370009)| ​| 25 FEB | RISKIQ | UNC1151/Ghostwriter (Belarus MoD) | [community.riskiq.com](https://community.riskiq.com/article/e3a7ceea/) | ​| 25 FEB | MalwareHunterTeam | Unknown phishing | [twitter.com/malwrhunterteam](https://twitter.com/malwrhunterteam/status/1497235270416097287) | ​| 25 FEB | ESET | Unknown scammers | [twitter.com/ESETresearch](https://twitter.com/ESETresearch/status/1497194165561659394) | ​| 25 FEB | BitDefender | Unknown scammers | [blog.bitdefender.com](https://blog.bitdefender.com/blog/hotforsecurity/cybercriminals-deploy-spam-campaign-as-tens-of-thousands-of-ukrainians-seek-refuge-in-neighboring-countries/) | ​| 25 FEB | SSSCIP Ukraine | Unkown phishing | [twitter.com/dsszzi](https://twitter.com/dsszzi/status/1497103078029291522) | ​| 25 FEB | RaidForums | Data broker "NetSec"  offering FSB (likely SMTP accounts) | RaidForums [not linked] | ​| 25 FEB | Zscaler | PartyTicket decoy ransomware | [zscaler.com](https://www.zscaler.com/blogs/security-research/technical-analysis-partyticket-ransomware) | ​| 25 FEB | INCERT GIE | Cyclops Blink, HermeticWiper | [linkedin.com](https://www.linkedin.com/posts/activity-6902989337210740736-XohK) [Login Required] | ​| 25 FEB | Proofpoint | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/threatinsight](https://twitter.com/threatinsight/status/1497355737844133895?s=20&t=Ubi0tb_XxGCbHLnUoQVp8w) | ​| 25 FEB | @fr0gger_ | HermeticWiper capabilities Overview | [twitter.com/fr0gger_](https://twitter.com/fr0gger_/status/1497121876870832128?s=20&t=_296n0bPeUgdXleX02M9mg) ​| 26 FEB | BBC Journalist | A fake Telegram account claiming to be President Zelensky is posting dubious messages | [twitter.com/shayan86](https://twitter.com/shayan86/status/1497485340738785283?s=21) | ​| 26 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT_UA Facebook](https://facebook.com/story.php?story_fbid=313517477474184&id=100064478028712) | ​| 26 FEB | MHT and TRMLabs | Unknown scammers, linked to ransomware | [twitter.com/joes_mcgill](https://twitter.com/joes_mcgill/status/1497609555856932864?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 26 FEB | US CISA | WhisperGate wiper, HermeticWiper | [cisa.gov](https://www.cisa.gov/uscert/ncas/alerts/aa22-057a) | ​| 26 FEB | Bloomberg | Destructive malware (possibly HermeticWiper) deployed at Ukrainian Ministry of Internal Affairs & data stolen from Ukrainian telecommunications networks | [bloomberg.com](https://www.bloomberg.com/news/articles/2022-02-26/hackers-destroyed-data-at-key-ukraine-agency-before-invasion?sref=ylv224K8) | ​| 26 FEB | Vice Prime Minister of Ukraine | IT ARMY of Ukraine created to crowdsource offensive operations against Russian infrastructure | [twitter.com/FedorovMykhailo](https://twitter.com/FedorovMykhailo/status/1497642156076511233) | ​| 26 FEB | Yoroi | HermeticWiper | [yoroi.company](https://yoroi.company/research/diskkill-hermeticwiper-a-disruptive-cyber-weapon-targeting-ukraines-critical-infrastructures) | ​| 27 FEB | LockBit [themselves] | LockBit ransomware | LockBit .onion [not linked] |  ​| 27 FEB | ALPHV [themselves] | ALPHV ransomware | vHUMINT [closed source] | ​| 27 FEB | Mēris Botnet [themselves] | DDoS attacks | vHUMINT [closed source] | ​| 28 FEB | Horizon News [themselves] | Leak of China's Censorship Order about Ukraine | [TechARP](https://www-techarp-com.cdn.ampproject.org/c/s/www.techarp.com/internet/chinese-media-leaks-ukraine-censor/?amp=1)| ​| 28 FEB | Microsoft | FoxBlade (aka HermeticWiper) | [Microsoft](https://blogs.microsoft.com/on-the-issues/2022/02/28/ukraine-russia-digital-war-cyberattacks/?preview_id=65075) | ​| 28 FEB | @heymingwei | Potential BGP hijacks attempts against Ukrainian Internet Names Center | [https://twitter.com/heymingwei](https://twitter.com/heymingwei/status/1498362715198263300?s=20&t=Ju31gTurYc8Aq_yZMbvbxg) | ​| 28 FEB | @cyberknow20 | Stormous ransomware targets Ukraine Ministry of Foreign Affairs | [twitter.com/cyberknow20](https://twitter.com/cyberknow20/status/1498434090206314498?s=21) |  ​| 1 MAR | ESET | IsaacWiper and HermeticWizard | [welivesecurity.com](https://www.welivesecurity.com/2022/03/01/isaacwiper-hermeticwizard-wiper-worm-targeting-ukraine/) | ​| 1 MAR | Proofpoint | Ukrainian armed service member's email compromised and sent malspam containing the SunSeed malware (likely TA445/UNC1151/Ghostwriter) | [proofpoint.com](https://www.proofpoint.com/us/blog/threat-insight/asylum-ambuscade-state-actor-uses-compromised-private-ukrainian-military-emails) | ​| 1 MAR | Elastic | HermeticWiper | [elastic.github.io](https://elastic.github.io/security-research/intelligence/2022/03/01.hermeticwiper-targets-ukraine/article/) | ​| 1 MAR | CrowdStrike | PartyTicket (aka HermeticRansom), DriveSlayer (aka HermeticWiper) | [CrowdStrike](https://www.crowdstrike.com/blog/how-to-decrypt-the-partyticket-ransomware-targeting-ukraine/) | ​| 2 MAR | Zscaler | DanaBot operators launch DDoS attacks against the Ukrainian Ministry of Defense | [zscaler.com](https://www.zscaler.com/blogs/security-research/danabot-launches-ddos-attack-against-ukrainian-ministry-defense) | ​| 3 MAR | @ShadowChasing1 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/ShadowChasing1](https://twitter.com/ShadowChasing1/status/1499361093059153921) | ​| 3 MAR | @vxunderground | News website in Poland was reportedly compromised and the threat actor uploaded anti-Ukrainian propaganda | [twitter.com/vxunderground](https://twitter.com/vxunderground/status/1499374914758918151?s=20&t=jyy9Hnpzy-5P1gcx19bvIA) | ​| 3 MAR | @kylaintheburgh | Russian botnet on Twitter is pushing "#istandwithputin" and "#istandwithrussia" propaganda (in English) | [twitter.com/kylaintheburgh](https://twitter.com/kylaintheburgh/status/1499350578371067906?s=21) | ​| 3 MAR | @tracerspiff | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com](https://twitter.com/tracerspiff/status/1499444876810854408?s=21) | ​####​ ​`Access Brokers` ​| Date | Threat(s) | Source | ​| --- | --- | --- | ​| 23 JAN | Access broker "Mont4na" offering UkrFerry | RaidForums [not linked] | ​| 23 JAN | Access broker "Mont4na" offering PrivatBank | RaidForums [not linked] | ​| 24 JAN | Access broker "Mont4na" offering DTEK | RaidForums [not linked] | ​| 27 FEB | KelvinSecurity Sharing list of IP cameras in Ukraine | vHUMINT [closed source] | ​| 28 FEB | "w1nte4mute" looking to buy access to UA and NATO countries (likely ransomware affiliate) | vHUMINT [closed source] | ​####​ ​`Data Brokers` ​| Threat Actor    | Type            | Observation                                                                                               | Validated | Relevance                     | Source                                                     | ​| --------------- | --------------- | --------------------------------------------------------------------------------------------------------- | --------- | ----------------------------- | ---------------------------------------------------------- | ​| aguyinachair    | UA data sharing | PII DB of ukraine.com (shared as part of a generic compilation)                                           | No        | TA discussion in past 90 days | ELeaks Forum \[not linked\]                                | ​| an3key          | UA data sharing | DB of Ministry of Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)           | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| an3key          | UA data sharing | DB of Ukrainian Ministry of Internal Affairs (wanted\[.\]mvs\[.\]gov\[.\]ua)                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (40M) of PrivatBank customers (privatbank\[.\]ua)                                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | DB of "border crossing" DBs of DPR and LPR                                                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (7.5M) of Ukrainian passports                                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB of Ukrainian car registration, license plates, Ukrainian traffic police records                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (2.1M) of Ukrainian citizens                                                                       | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (28M) of Ukrainian citizens (passports, drivers licenses, photos)                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (1M) of Ukrainian postal/courier service customers (novaposhta\[.\]ua)                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (10M) of Ukrainian telecom customers (vodafone\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (3M) of Ukrainian telecom customers (lifecell\[.\]ua)                                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (13M) of Ukrainian telecom customers (kyivstar\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| danieltx51      | UA data sharing | DB of Ministry of Foreign Affairs of Ukraine (mfa\[.\]gov\[.\]ua)                                         | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| DueDiligenceCIS | UA data sharing | PII DB (63M) of Ukrainian citizens (name, DOB, birth country, phone, TIN, passport, family, etc)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Featherine      | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Internal Affairs of Ukraine public data search engine (wanted\[.\]mvs\[.\]gov\[.\]ua)  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Motor Insurance Bureau of Ukraine (mtsbu\[.\]ua)                                                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | PII DB of Ukrainian digital-medicine provider (medstar\[.\]ua)                                            | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of ticket.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of id.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of my.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of anti-violence-map.msp.gov.ua                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dopomoga.msp.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-services.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of edu.msp.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of education.msp.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ek-cbi.msp.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mail.msp.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal-gromady.msp.gov.ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of web-minsoc.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wcs-wim.dsbt.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich.com                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsns.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of zt.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mvs.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsbt.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of forest.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nkrzi.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dabi.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of comin.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dp.dpss.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of esbu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mms.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mova.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mspu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nads.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of reintegration.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sies.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sport.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mepr.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of va.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cg.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ch-tmo.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cp.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cpd.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of hutirvilnij-mrc.mvs.gov.ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dndekc.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of visnyk.dndekc.mvs.gov.ua                                                                            | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dpvs.hsc.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of odk.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-driver\[.\]hsc\[.\]gov\[.\]ua                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wanted\[.\]mvs\[.\]gov\[.\]ua                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minregeion\[.\]gov\[.\]ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of health\[.\]mia\[.\]solutions                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtsbu\[.\]ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich\[.\]com                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kyivcity\[.\]com                                                                                    | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr\[.\]mvs\[.\]gov\[.\]ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of gkh\[.\]in\[.\]ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro\[.\]gov\[.\]ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| Intel\_Data     | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Kristina        | UA data sharing | DB of Ukrainian National Police (mvs\[.\]gov\[.\]ua)                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| NetSec          | UA data sharing | PII DB (53M) of Ukrainian citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Psycho\_Killer  | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | Exploit Forum .onion \[not linked\]                        | ​| Sp333           | UA data sharing | PII DB of Ukrainian and Russian interpreters, translators, and tour guides                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine \[copy\]     | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua) \[copy\] | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​####​ ​`Vendor Support` ​| Vendor | Offering | URL | ​| --- | --- | --- | ​| Dragos | Access to Dragos service if from US/UK/ANZ and in need of ICS cybersecurity support | [twitter.com/RobertMLee](https://twitter.com/RobertMLee/status/1496862093588455429) | ​| GreyNoise |  Any and all `Ukrainian` emails registered to GreyNoise have been upgraded to VIP which includes full, uncapped enterprise access to all GreyNoise products | [twitter.com/Andrew___Morris](https://twitter.com/Andrew___Morris/status/1496923545712091139) | ​| Recorded Future | Providing free intelligence-driven insights, perspectives, and mitigation strategies as the situation in Ukraine evolves| [recordedfuture.com](https://www.recordedfuture.com/ukraine/) | ​| Flashpoint | Free Access to Flashpoint’s Latest Threat Intel on Ukraine | [go.flashpoint-intel.com](https://go.flashpoint-intel.com/trial/access/30days) | ​| ThreatABLE | A Ukraine tag for free threat intelligence feed that's more highly curated to cyber| [twitter.com/threatable](https://twitter.com/threatable/status/1497233721803644950) | ​| Orange | IOCs related to Russia-Ukraine 2022 conflict extracted from our Datalake Threat Intelligence platform. | [github.com/Orange-Cyberdefense](https://github.com/Orange-Cyberdefense/russia-ukraine_IOCs)| ​| FSecure | F-Secure FREEDOME VPN is now available for free in all of Ukraine | [twitter.com/FSecure](https://twitter.com/FSecure/status/1497248407303462960) | ​| Multiple vendors | List of vendors offering their services to Ukraine for free, put together by [@chrisculling](https://twitter.com/chrisculling/status/1497023038323404803) | [docs.google.com/spreadsheets](https://docs.google.com/spreadsheets/d/18WYY9p1_DLwB6dnXoiiOAoWYD8X0voXtoDl_ZQzjzUQ/edit#gid=0) | ​| Mandiant | Free threat intelligence, webinar and guidance for defensive measures relevant to the situation in Ukraine. | [mandiant.com](https://www.mandiant.com/resources/insights/ukraine-crisis-resource-center) | ​| Starlink | Satellite internet constellation operated by SpaceX providing satellite Internet access coverage to Ukraine | [twitter.com/elonmusk](https://twitter.com/elonmusk/status/1497701484003213317) | ​| Romania DNSC | Romania’s DNSC – in partnership with Bitdefender – will provide technical consulting, threat intelligence and, free of charge, cybersecurity technology to any business, government institution or private citizen of Ukraine for as long as it is necessary. | [Romania's DNSC Press Release](https://dnsc.ro/citeste/press-release-dnsc-and-bitdefender-work-together-in-support-of-ukraine)| ​| BitDefender | Access to Bitdefender technical consulting, threat intelligence and both consumer and enterprise cybersecurity technology | [bitdefender.com/ukraine/](https://www.bitdefender.com/ukraine/) | ​| NameCheap | Free anonymous hosting and domain name registration to any anti-Putin anti-regime and protest websites for anyone located within Russia and Belarus | [twitter.com/Namecheap](https://twitter.com/Namecheap/status/1498998414020861953) | ​| Avast | Free decryptor for PartyTicket ransomware | [decoded.avast.io](https://decoded.avast.io/threatresearch/help-for-ukraine-free-decryptor-for-hermeticransom-ransomware/) |  ​####​ ​`Vetted OSINT Sources` ​| Handle | Affiliation | ​| --- | --- | ​| [@KyivIndependent](https://twitter.com/KyivIndependent) | English-language journalism in Ukraine | ​| [@IAPonomarenko](https://twitter.com/IAPonomarenko) | Defense reporter with The Kyiv Independent | ​| [@KyivPost](https://twitter.com/KyivPost) | English-language journalism in Ukraine | ​| [@Shayan86](https://twitter.com/Shayan86) | BBC World News Disinformation journalist | ​| [@Liveuamap](https://twitter.com/Liveuamap) | Live Universal Awareness Map (“Liveuamap”) independent global news and information site | ​| [@DAlperovitch](https://twitter.com/DAlperovitch) | The Alperovitch Institute for Cybersecurity Studies, Founder & Former CTO of CrowdStrike | ​| [@COUPSURE](https://twitter.com/COUPSURE) | OSINT investigator for Centre for Information Resilience | ​| [@netblocks](https://twitter.com/netblocks) | London-based Internet's Observatory | ​####​ ​`Miscellaneous Resources` ​| Source | URL | Content | ​| --- | --- | --- | ​| PowerOutages.com | https://poweroutage.com/ua | Tracking PowerOutages across Ukraine | ​| Monash IP Observatory | https://twitter.com/IP_Observatory | Tracking IP address outages across Ukraine | ​| Project Owl Discord | https://discord.com/invite/projectowl | Tracking foreign policy, geopolitical events, military and governments, using a Discord-based crowdsourced approach, with a current emphasis on Ukraine and Russia | ​| russianwarchatter.info | https://www.russianwarchatter.info/ | Known Russian Military Radio Frequencies |

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

RTAA-72, is CVCIO's real-time intelligence dashboard for Twitter

OSINT - SOCMINT (social media intelligence) Twitter hashtag analyzer

X Intelligence CLI — search, monitor, analyze, and engage on X/Twitter. Single Rust binary, 2.5MB, <5ms startup. AI agent skill.

Low-intelligence Twitter bot. :bird:

Threat intelligence collecter from twitter Streaming API.

Twitter is an online social networking service with over 300 million monthly active users. This enormous amount of data available on social media platforms can be extracted and analyzed for various purposes. In this paper, we aim to investigate the relationship between sentiment analysis of Twitter data and stock market prices for five companies (Walmart, ExxonMobil, Apple, Berkshire Hathaway Inc., and Amazon) by scraping the Tweets extracted from Twitter based on company hashtags and using the twitter intelligence tool – twint. Sentiment analysis is applied to the extracted tweets and a correlation is analyzed between stock market movements of a company and sentiments in tweets. Elaborately, news and tweets in social media about a company would encourage decision of people to invest or not in the stocks of that company and as a result, the stock price of that company would increase or fall. At the end of the paper, it is shown that a none or very weak correlation exists between the rise and fall in stock prices with the public sentiments in tweets

Threat Intelligence ile ilgilenenlerin takip etmesini önerdiğim kullanıcı adlarının listesi

Twitter data for signals intelligence

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

# Problem: Predicting Credit Card Fraud ## Introduction to business scenario You work for a multinational bank. There has been a significant increase in the number of customers experiencing credit card fraud over the last few months. A major news outlet even recently published a story about the credit card fraud you and other banks are experiencing. As a response to this situation, you have been tasked to solve part of this problem by leveraging machine learning to identify fraudulent credit card transactions before they have a larger impact on your company. You have been given access to a dataset of past credit card transactions, which you can use to train a machine learning model to predict if transactions are fraudulent or not. ## About this dataset The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred over the course of two days and includes examples of both fraudulent and legitimate transactions. ### Features The dataset contains over 30 numerical features, most of which have undergone principal component analysis (PCA) transformations because of personal privacy issues with the data. The only features that have not been transformed with PCA are 'Time' and 'Amount'. The feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction amount. 'Class' is the response or target variable, and it takes a value of '1' in cases of fraud and '0' otherwise. Features: `V1, V2, ... V28`: Principal components obtained with PCA Non-PCA features: - `Time`: Seconds elapsed between each transaction and the first transaction in the dataset, $T_x - t_0$ - `Amount`: Transaction amount; this feature can be used for example-dependent cost-sensitive learning - `Class`: Target variable where `Fraud = 1` and `Not Fraud = 0` ### Dataset attributions Website: https://www.openml.org/d/1597 Twitter: https://twitter.com/dalpozz/status/645542397569593344 Authors: Andrea Dal Pozzolo, Olivier Caelen, and Gianluca Bontempi Source: Credit card fraud detection - June 25, 2015 Official citation: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015. The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.

Twitter, Instagram, and GeoTagging Media Intelligence. One-stop information about Social Networking sites.

Project "Twitter Sentiment Classification" in the Computational Intelligence Lab of ETH Zurich, Spring 2018

This tool is designed for systematic investigation and analysis of Twitter accounts and activities through legitimate open-source intelligence (OSINT) methods.

X / Twitter Social Intelligence & Large-Scale Scraping Engine

Automated Threat Intelligence collection and analysis from Twitter data

X/Twitter topic intelligence skill for Claude Code. Scrapes real tweets, engagement metrics, and KOL accounts via Playwright browser automation.

MAN PROBLEM? Technology advancement to address the world’s rising demand for clean and reasonable power will need simultaneous improvements in materials science and technology in order to meet the achievement demands of new power-generating systems. For instance, a technological advance in the wireless broadband industry might enable more productive use of insufficient range, resulting in greater speed and capability for wireless broadband There are so numerous methods technological advancements have improved humanity, save time and cost of production. Our individual life is now massively dependent on technology and technology has advanced with years and transformed the way we practice, travel, socialize, educate our children live, and buy products. As somebody’s demands for developing technology recommence, the Institute of Electrical and Electronics Engineers (IEEE’s) presents a clear knowledge as an industry ready to continue developing patterns for the computer and science industry that foster technological discovery and perfection for the benefit of humanity. Advancing technology for the advantage of humanity Technology transforms the way individuals talk, learn, and imagine. It helps the community and ascertains show people cooperate with each other on a periodic basis. Technology plays a vital role in society. The invention of the internet supplies us access to data at a twenty-four-hour rate and you have access to almost anything online. technological advancements initiated learning more entertainment and beneficial. Technology brought many new modes of electronic intelligence. For example, there is social networking, emails. you can facetime a personality that lives on the other side of the globe, and there’s video conferencing where you can have discussions electronically. There are many innovative apps on telephones that although characters to watch their weight, how many calories they intake, their heart rate and other health resources any time of the day. technology provides us with a lot of educational content, fast and free, which makes the learning process more personalized and self-paced, improving it. technology has made life easier mostly for those who get the chance to interact with it. For instance use of social media or websites in our daily lives. young people who become addicted to social media ( media platforms such as TikTok, Instagram, Twitter, Facebook, Snapchat and others are highly addictive, and YouTube can also be grouped into this category. Every time a person takes engagement via a “comment” “follow” or “like” somebody’s brain delivers feel-good neurotransmitters, eventually resulting in wanting more engagement). they crave to cope with the dynamic society where outward appearance is highly regarded. They do all kinds of things in order to become successful and get more likes some even grow cyberbullied because of their visions. This is why modern people suffer more from grief to the point of suicide. HOW ADVANCED TECHNOLOGY HAS ADDED THE MAN’S PROBLEM? Humans are the most magnificent piece of God. Nature has given humans all the demanded things that they required. But the eagerness of humans was not fulfilled. They started making technology in order to get their work done more spontaneously. There are so many interests in Technology. But remember if there is a benefit, there problem also. So the obstacle is people became inactive because of technology. Nowadays, we do not go to the supermarket to purchase groceries rather than order them online. We didn’t go out and meet our associates “face to face” alternatively that we meet them on social media “ONLINE” or “LIVE”. The human mind has built technology but now technology is destroying our minds. Technology has also created some new complications for humans. We don’t believe in our own people instead we consider technology which can be replaced by itself also. Nowadays, there are several Hackers on the Internet. They can take your secret data also. Conclusion. we are in the epoch of globalization, where technologies will always be done, and these have designed a great change in human action, either for good or for bad. Technology has promoted many things, and entering in context has served much, in distance education; however, technology can become serious because sometimes it can create problems for the man if it is not used suitably, following rules of their use.

DIGITAL MARKETING BY SARA ATIQ INTRODUCTION “It is a marketing technique that involves usage of digital mediums such as internet & wireless for creating awareness, consideration, purchase & loyalty for a brand product or a service". It is the term used to describe any marketing efforts that place on the internet or a digital device. It has different channels that enable the business to entice their customer into buying their product & services. Philip Kotler is considered the father of digital marketing who is the author of 60 marketing books and provides us important lessons that can be applied to our digital strategy. Before digital marketing, we have Traditional marketing, which is a conventional mode of marketing that helps to reach out to the semi-targeted audience with various offline advertising & promotion modes. CONSTITUENT OF DIGITAL MARKETING TRAFFIC ACQUISITION CHANNELS SEARCH ENGINE MARKETING(SEM): It is a form of internet marketing that involves the promotion of websites by increasing their visibility in search engine result pages (SERP) primarily through paid advertising. SEM may incorporate search engine optimization (SEO), which adjusts or rewrites website content and site architecture to achieve a higher ranking in search engine result pages to enhance pay per click. SOCIAL MEDIA MARKETING: Social media marketing involves the use of social media platforms to connect with the audience to build your brand, increase sales & drive website traffic. It also allows to publish great content on social media platforms & run social media advertisements. Major social media platforms are Facebook, Instagram, Twitter, LinkedIn, Pinterest, YouTube, and Snapchat. EMAIL MARKETING: It is an act of sending a commercial message particularly to a group of people, using email. It involves using emails to send advertisements, request business, or sales, or donations. It usually refers to sending email to enhance a merchant's relationship with a current or previous customer, encouraging customer loyalty, acquiring new customers, or convincing new customers to purchase something immediately. DISPLAY ADVERTISING: It is an online form of advertising in which a company's Ads appear on third-party sites or appear on the search engine result page such as publishers or social networks. This advertisement can increase the website page view of a company from most types of customers except the non-unauthenticated visitor who visits the site before. The main purpose of display advertising is to support brand awareness and it also helps to increase the purchase, intention of the consumers. AFFILIATE MARKETING: It is a type of performance-based marketing in which a business reward one or more affiliates for each visitor or customer brought by the affiliate's marketing efforts. The internet has increased the prominence of affiliate marketing. Amazon popularized the practice by creating the affiliate marketing program whereby the website and bloggers put the link to the Amazon page for a reviewed product to receive an advertising fee when a purchase is made. So, it is essentially a pay-for-performance marketing program where the act of selling is outsourced across a vast network. SUPPORTING CHANNELS MOBILE MARKETING: Mobile marketing is a multi-channel, digital marketing strategy aimed at reaching a target audience on their smartphones, tablets, or other mobile devices via websites, email, social media, and Apps. Mobile marketing is an important piece of the puzzle when it comes to building out any short-term or long-term marketing plan. From email to pay per click (PPC), search engine optimization (SEO)content marketing, and social media marketing, there is a mobile marketing channel to reach every part of your audience where they are most comfortable. mobile marketing can do wonders to drive brand value. WEBSITE: Website is the must-have tool for your business as it provides you with a dedicated platform where you can educate your audience about your brands, products, and services. This requires a solid understanding of your target audience and an effective content marketing strategy. Your website is an ideal channel for your content marketing campaigns. Through blogs, posts, and announcements you can provide existing and potential customers with valuable and relevant content to help them solve their pain points. Because websites have multimedia capabilities you can easily distribute different types of content in the form of articles, infographics, and even videos. If your website will have high-quality relevant and insightful content then your website will have increased organic traffic. WEB ANALYTICS: Web analytics is the measurement, collection, analysis, and reporting of internet data for understanding and optimizing web usage. The focus of web analytics is to understand the users of a site, their behavior, and their activities. The study of online user behavior and activities generate valuable marketing intelligence and provide - Performance measures of the website against the target. Insight on user behaviors and needs, and how the sight meets those needs. Optimization ability to make modifications to improve the website based on the result. Web analytics tools offer hundreds of metrics. all of them are interesting but only a few would be useful for measuring website performance. PROCESS FRAMEWORK OF DIGITAL MARKETING The framework of digital marketing is based on the 4 main objectives of digital marketing. 1.awareness 3. purchase 2.consideration 4. loyalty Loyalty Buyer -> loyal customer Purchase Interested -> buyer Awareness Unaware -> aware Consideration Aware -> interested

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