No description available

It is a project of detecting phishing websites which are main cause of cyber security attacks. It is done using Machine learning with Python

This Project is a Final Year Project on Detection of Phishing Website Using Machine Learning, Copyright (c) 2021 Goodness Adediran All rights reserved.

Machine Learning for Phishing Website Detection

Phishing Website Detection using Python and Flask

advanced AI-powered phishing detection system designed specifically to protect Nigerian internet users from cyber threats, phishing attacks, and online fraud. The platform uses machine learning algorithms to analyze websites and provide real-time security assessments.

Phishing website detection system provides strong security mechanism to detect and prevent phishing domains from reaching user. This project presents a simple and portable approach to detect spoofed webpages and solve security vulnerabilities using Machine Learning. It can be easily operated by anyone since all the major tasks are happening in the backend. The user is required to provide URL as input to the GUI and click on submit button. The output is shown as “YES” for phishing URL and “NO” for not phished URL. PYTHON DEPENDENCIES: • NumPy, Pandas, Scikit-learn: For Data cleaning, Data analysis and Data modelling. • Pickle: For exporting the model to local machine • Tkinter, Pyqt, QtDesigner: For building up the Graphical User Interface (GUI) of the software. To avoid the pain of installing independent packages and libraries of python, install Anaconda from www.anaconda.com. It is a Python data science platform which has all the ML libraries, Data analysis libraries, Jupyter Notebooks, Spyder etc. built in it which makes it easy to use and efficient. Steps to be followed for running the code of the software: • Install anaconda in the system. • gui.py : It contains the code for the GUI and is linked to other modules of the software. • Feature_extractor.py: It contains the code of Data analysis and data modelling. • Rf_model.py: It contains the trained machine learning model. • Only gui.py is to be run to execute the whole software.

HookPhish is a Python script designed to aid in the detection of phishing websites

This is an End-to-End Machine Learning Project which focuses on phishing websites to classify phishing and legitimate ones. Particularly, I focused on content-based features like html tag based features. You can find feature extraction, data collection, preparation process here. Also, building ML models, evaluating them are available here.

A project that predicts a phishing URL by extracting 17 features in 3 different categories and then train and test the machine learning models using a dataset from Phishtank.

This project is based on detecting phishing/fraud/malicious website using Random Forest Classification formula. Implemented using Python programming language and Django framework.

Experiments to detect phishing websites using neural networks

Using Machine learning classifier developed GUI which takes the url of suspicious websites as input and tells the user if it is a benign or malicious website and thus prevents the users from accessing malicious websites.

Phishing websites are fraudulent sites that impersonate a trusted party to gain access to sensitive information of an individual person or organization. Traditionally, phishing website detection is done through the usage of blacklist databases. However, due to the current, rapid development of global networking and communication technologies, there are numerous websites and it has become difficult to classify based on traditional methods since new websites are created every second. In this paper, we are proposing a real-time, anti-phishing system. In the first step, we extract the lexical and host-based properties of a website. In the second step, we combine URL (Uniform Resource Locator) features, NLP and host-based properties to train the machine learning and deep learning models. Our detection model is able to detect phishing URLs with a detection rate of 94.89%.

This System Contains The Full Project of Phishing Website Detection with the GUI

Phishing Website detection with Machine Learning. This include flask server, chrome extension and machine learning for phishing website detection.

Building our final year project on phishing website detection using machine learning

Cyberex Secure is planned to be a security solution with a wide range of features such as phishing detection, advanced AI- face recognition while accessing payment gateways, computer generated passwords and automatic sign-in. The software will automatically block and report phishing emails/websites to the government portal so that they can be taken down. The assistant will ask for a prompt to send the report before sending it to the portal.

In todays era, due to the surge in the usage of internet and other online platforms, security has been a major concern. Many cyber attacks take place each day out of which website phishing is the most common issue. It is an act of imitating a legitimate website and thereby duping the users and stealing their sensitive information. So, with respect to this problem this paper will introduce a possible solution in order to avoid such attacks by checking whether the provided URLs are phishing URL or legitimate URL. It is basically a Machine Learning based system particularly supervised learning where we have provided 2000 phishing and 2000 legitimate URL dataset. We have taken into consideration Random Forest algorithm due to its performance and accuracy. It takes 9 features into consideration and hence detects whether the URL is safe to access or a phishing URL.

No description available

Phishing is a website forgery with an intention to track and steal the sensitive information of online users. It is a form of identity theft, in which criminals build replicas of target websites and lure unsuspecting victims to disclose their sensitive information like passwords, PIN, etc. A huge volume of information is downloaded and uploaded constantly to the web. This gives opportunities for criminals to hack important personal information. To overcome the issues faced here, developed a phishing websites detection technique based on machine learning classifiers with a wrapper features selection method. Classification algorithms used are Artificial Neural Network, Random Forest and Support Vector Machine. Dynamic features extraction is made from the entered URL and the trained model is used for the detection of phishing URL.

An extension for detection phishing website

Offline phishing detection model for websites using a hybrid CNN–LSTM architecture. Operates without internet access, classifying URLs as legitimate or potentially malicious based on learned patterns.

Starter repository for Manning LP: Use Machine Learning to Detect Phishing Websites

Final master's degree project. Machine learning models and techniques

PREDCIT THE WEBSITE URL IT IS FAKE OR SAFE.

A web application to predicted whether a URL/Website is phishing or not by extracting its lexical features.

Malicious URL, a.k.a. malicious website, is a common and serious threat to cyber-security. Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This project aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. i present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.).

Cybersecurity threats, especially phishing attacks, are one of the most critical issues IT companies face today. Phishing emails and fake websites trick users into revealing confidential information, leading to massive financial and data losses. PhishGuard AI is a real-time phishing detection platform that uses AI to scan emails, links.

This project is a browser-based phishing detection system implemented as a Chrome Extension that leverages machine learning models to identify and block phishing websites in real-time. It is part of a research initiative focused on enhancing web security through intelligent URL and content-based analysis.