ISC2-CC-Study-Material

Cilium Certified Associate Certification Study Guide

The content in this repo is based on the self-paced course called Certified in Cybersecurity from (ISC)2, which is a preparation for (ISC)2 Certified in Cybersecurity. And Also Dump Question which will help student to get better preparaion for this vendor examination.

Study notes about LLVM. LLVM 学习笔记. Licensed under CC BY-NC-SA 4.0

My learning, tutorials on Cybersecurity

Resources used to pass the Cisco ENCOR exam

CCSP study resources, notes, exam material.

PineScript Study, Indicator & Strategy, under licence CC BY-NC 4.0

study guide for CNA

Disclaimer: Please note that these materials are public available in several reference websites. These materials are not only the source for CCSP certification, there could be more materials to refer. All these materials are solely used by myself while preparing for CCSP. Please follow official ISC2 materials from official website.

Cloud computing (CC) has recently been receiving tremendous attention from the IT trade and educational researchers. CC leverages its distinctive services to cloud customers in a very pay-as-you-go, anytime and anyplace manner. As well as Cloud services offer dynamically scalable services on demand. Therefore, service supplying plays a key role in CC. Then, it is good opportunity for customers to find suitable and lowly cost service for their project. Specially, Customer must be able to select appropriate cloud service according to their needs and money. It is time-consuming task for consumers to collect the necessary information and analyze from all cloud service providers to make right decision. As well as it is also a highly demanding task from a computational perspective because multiple consumers who have similar requirements conduct same computations repeatedly. They provide all products you might need for moving your business to the cloud. But these product offerings differ in pricing as well as the naming of their services. Some Businessmen already may use on-premises infrastructure or think which infrastructure will use for my project. They may have more complex problems like how to choose a cloud service, which services want use and specially how many costs want to pay for monthly or yearly. Sometimes, someone already use a cloud services, they have lot of problems like more expensive, less flexibility, hard to use, overwhelming options of services, poor management of GUI and tool, complex price schema and other issues. However, they must spend more price and time as useless. Because they could not select best cloud service provider early to their business. For solving the cloud service selection problem, many researchers have proposed some approaches including multicriteria decision analysis (MCDA) and Brokerage-Based Approach. But we cannot see any machine learning prediction system for solving this issue. This system enables the user to choose from among a number of available choices. In this paper, we make a neural network with TensorFlow to service selection in CC. This system focuses on three main players in CC. There are Amazon Web Services, Microsoft Azure and Google Cloud Platform in the race for cloud services providers. I identify and synthesize several products relevant for web services in Cloud providers. There are Featured, Compute, Storage, Database, Networking, Operation, Identity & Access and Cost. As well as I focus on Small and medium-sized businesses (SMBs). Because these are most aggressive segment in cloud service. It is less-complex IT needs, fewer legacy applications and less IT support than larger enterprises. We use Support Vector Machine (SVM), Multiple linear regression (MLR) and Multiple-criteria decision analysis (MCDA). We develop efficient and flexible recommendation system for ranking cloud service providers. I prove accuracy and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.

An comparative study on the paper Exploring Generalization in Deep Learning https://papers.nips.cc/paper/7176-exploring-generalization-in-deep-learning.pdf

📘 Study guide and self-assessment portfolio for the ISC2 Certified in Cybersecurity (CC) exam. Includes notes, diagrams, practice questions, quizzes, and a structured study plan.

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An AI prompt featuring Pudgy, a witty hamster hacker, for engaging (ISC)² CC exam study sessions. Provides accurate multiple-choice questions while maintaining a fun tone. Aims to make learning enjoyable and effective for exam preparation.

Source code of @anthropic-ai/claude-code v2.1.88

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This repository contains concise notes and key concepts for preparing for the ISC2 Certified in Cybersecurity (CC) exam. The notes cover essential topics, security principles, and practices, designed to help you study effectively for the certification.

List of CCNP Resources I used.

Study Guide for the AWS Certified Cloud Practitioner

ccxt是最强大的交易所交易api的工具。

Prepare for the ISC2 Certified in Cybersecurity (CC) exam with up-to-date study resources and expert guidance. Learn essential cybersecurity concepts, practice with real exam-style questions, and boost your chances of success on your first attempt.

Problem Statement Understanding the consumption pattern for credit cards at an individual consumer level is important for customer relationship management. This understanding allows banks to customize for consumers and make strategic marketing plans. Thus it is imperative to study the relationship between the characteristics of the consumers and their consumption patterns. Here the dataset is of some XYZ Bank that has given a sample of their customers, along with their details like age, gender and other demographics. Also shared are information on liabilities, assets and history of transactions with the bank for each customer. In addition to the above, data has been provided for a particular set of customers' credit card spend in the previous 3 months (April, May & June) and their expected average spend in the coming 3 months (July, August & September). The average spend for different set of customers needs to be predicted in the test set for the coming 3 months. Data Dictionary id Unique ID for every Customer account_type Account Type – current or saving gender Gender of customer-M or F age Age of customer region_code Code assigned to region of residence (has order) cc_cons_apr Credit card spend in April dc_cons_apr Debit card spend in April cc_cons_may Credit card spend in May dc_cons_may Debit card spend in May cc_cons_jun Credit card spend in June dc_cons_jun Debit card spend in June cc_count_apr Number of credit card transactions in April cc_count_may Number of credit card transactions in May cc_count_jun Number of credit card transactions in June dc_count_apr Number of debit card transactions in April dc_count_may Number of debit card transactions in May dc_count_jun Number of debit card transactions in June card_lim Maximum Credit Card Limit allocated personal_loan_active Active personal loan with other bank vehicle_loan_active Active Vehicle loan with other bank personal_loan_closed Closed personal loan in last 12 months vehicle_loan_closed Closed vehicle loan in last 12 months investment_1 DEMAT investment in june investment_2 fixed deposit investment in june investment_3 Life Insurance investment in June investment_4 General Insurance Investment in June debit_amount_apr Total amount debited for April credit_amount_apr Total amount credited for April debit_count_apr Total number of times amount debited in april credit_count_apr Total number of times amount credited in april max_credit_amount_apr Maximum amount credited in April debit_amount_may Total amount debited for May credit_amount_may Total amount credited for May credit_count_may Total number of times amount credited in May debit_count_may Total number of times amount debited in May max_credit_amount_may Maximum amount credited in May debit_amount_jun Total amount debited for June credit_amount_jun Total amount credited for June credit_count_jun Total number of times amount credited in June debit_count_jun Total number of times amount debited in June max_credit_amount_jun Maximum amount credited in June loan_enq Loan enquiry in last 3 months (Y or N) emi_active Monthly EMI paid to other bank for active loans cc_cons (Target) Average Credit Card Spend in next three months Evaluation Metric Submissions are evaluated on Root Mean Squared Logarithmic Error(RMSLE) between the predicted credit card consumption and the observed target. Approach At first, I conducted exploratory data analysis of the dataset to gain a deeper understanding of the data. Next, I did feature engineering to create new variables.Then I tried some scikit-learn models out of which XGBoost and Random Forest gave good RMSLE. In the end I created a stacked model of those two with Linear Regression and it has been selected as the final model. RMSLE: 115.02

Research in automatic piano music generation has only recently started to involve piano pedals as a part of the task. In this work, we train various neural network architectures with piano sustain pedal control-change (CC) data using different categories within the MAESTRO classical piano music dataset to study the performances of basic models and test the suitability of neural networks in an automatic piano pedal styling task. By changing the temporal scanning range of convolution kernels and the depth of the network structure, we show that both factors are relevant in the accuracy of pedaling style prediction. Currently, our best CNN-based Auto-SusPed model predicts a specific composer’s pedaling style and a specific musical era’s style with accuracies of around 90%.

Browser Security Handbook Written and maintained by Michal Zalewski <lcamtuf@google.com>. Copyright 2008, 2009 Google Inc, rights reserved. Released under terms and conditions of the CC-3.0-BY license. Table of Contents → Part 1: Basic concepts behind web browsers → Part 2: Standard browser security features → Part 3: Experimental and legacy security mechanisms Introduction Hello, and welcome to the Browser Security Handbook! This document is meant to provide web application developers, browser engineers, and information security researchers with a one-stop reference to key security properties of contemporary web browsers. Insufficient understanding of these often poorly-documented characteristics is a major contributing factor to the prevalence of several classes of security vulnerabilities. Although all browsers implement roughly the same set of baseline features, there is relatively little standardization - or conformance to standards - when it comes to many of the less apparent implementation details. Furthermore, vendors routinely introduce proprietary tweaks or improvements that may interfere with existing features in non-obvious ways, and seldom provide a detailed discussion of potential problems. The current version of this document is based on the following versions of web browsers: | Browser | Version | Test date | Usage* | Notes | |:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:------------------------------------------------------|:------------------------------------------| | Microsoft Internet Explorer 6 | 6.0.2900.5512 | Feb 2, 2009 | 16% | | | Microsoft Internet Explorer 7 | 7.0.5730.11 | Dec 11, 2008 | 11% | | | Microsoft Internet Explorer 8 | 8.0.6001.18702 | Sep 7, 2010 | 28% | | | Mozilla Firefox 2 | 2.0.0.18 | Nov 28, 2008 | 1% | | | Mozilla Firefox 3 | 3.6.8 | Sep 7, 2010 | 22% | | | Apple Safari | 4.0 | Jun 10, 2009 | 5% | | | Opera | 9.62 | Nov 18, 2008 | 2% | | | Google Chrome | 7.0.503.0 | Sep 7, 2010 | 8% | | | Android embedded browser | SDK 1.5 R3 | Oct 3, 2009 | n/a | | * Approximate browser usage data based on public Net Applications estimates for August 2010. Disclaimers and typographical conventions Please note that although we tried to make this document as accurate as possible, some errors might have slipped through. Use this document only as an initial reference, and independently verify any characteristics you wish to depend upon. Test cases for properties featured in this document are freely available for download. The document attempts to capture the risks and security considerations present for general populace of users accessing the web with default browser settings in place. Although occasionally noted, the degree of flexibility offered through non-standard settings is by itself not a subject of this comparative study. Through the document, red color is used to bring attention to browser properties that seem particularly tricky or unexpected, and need to be carefully accounted for in server-side implementations. Whenever status quo appears to bear no significant security consequences and is well-understood, but a particular browser implementation takes additional steps to protect application developers, we use green color to denote this, likewise. Rest assured, neither of these color codes implies that a particular browser is less or more secure than its counterparts. Acknowledgments Browser Security Handbook would not be possible without the ideas and assistance from the following contributors: Filipe Almeida Brian Eaton Chris Evans Drew Hintz Nick Kralevich Marko Martin Tavis Ormandy Wladimir Palant David Ross Marius Schilder Parisa Tabriz Julien Tinnes Berend-Jan Wever Mike Wiacek The document builds on top of previous security research by Adam Barth, Collin Jackson, Amit Klein, Jesse Ruderman, and many other security experts who painstakingly dissected browser internals for the past few years.

Basic topology for CCNA and CCNP studies.

Personal notes and explanations on key **CCNA 200-301** topics, covering networking fundamentals, IP addressing, routing protocols, and troubleshooting techniques.

Personal study guide that you can use to revise for your AWS CCP certification exam.

CCNP ENCOR 350-401 Study Guide with EVE-NG Labs

The content in this repo is based on the self-paced course called Certified in Cybersecurity from (ISC)2, which is a preparation for (ISC)2 Certified in Cybersecurity. And Also Dump Question which will help student to get better preparation for this vendor examination.