Python For Cybersecurity Cookbook
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Python for Cybersecurity Cookbook
Learn how to use Python for vulnerability scanning, malware analysis, penetration testing, and more KEY FEATURES ● Get familiar with the different aspects of cybersecurity, such as network security, malware analysis, and penetration testing. ● Implement defensive strategies to protect systems, networks, and data from cyber threats. ● Discover advanced offensive techniques for penetration testing, exploiting vulnerabilities, and assessing overall security posture. DESCRIPTION Python is a powerful and versatile programming language that can be used for a wide variety of tasks, including general-purpose applications and specific use cases in cybersecurity. This book is a comprehensive guide to solving simple to moderate complexity problems in cybersecurity using Python. It starts with fundamental issues in reconnaissance and then moves on to the depths of the topics such as forensic analysis, malware and phishing analysis, and working with wireless devices. Furthermore, it also covers defensive and offensive security topics, such as system hardening, discovery and implementation, defensive security techniques, offensive security techniques, and penetration testing. By the end of this book, you will have a strong understanding of how to use Python for cybersecurity and be able to solve problems and create solutions independently. WHAT YOU WILL LEARN ● Learn how to use Python for cyber forensic analysis. ● Explore ways to analyze malware and phishing-based compromises. ● Use network utilities to gather information, monitor network activity, and troubleshoot issues. ● Learn how to extract and analyze hidden information in digital files. ● Examine source code for vulnerabilities and reverse engineering to understand software behavior. WHO THIS BOOK IS FOR The book is for a wide range of people interested in cybersecurity, including professionals, researchers, educators, students, and those considering a career in the field. TABLE OF CONTENTS 1. Getting Started 2. Passive Reconnaissance 3. Active Reconnaissance 4. Development Environment for Advanced Techniques 5. Forensic Analysis 6. Metadata Extraction and Parsing 7. Malware and Phishing Analysis 8. Working with Wireless Devices 9. Working with Network Utilities 10. Source Code Review and Reverse Engineering 11. System Hardening, Discovery, and Implementation 12. Defensive Security Techniques 13. Offensive Security Techniques and Pen Testing
Machine Learning for Cybersecurity Cookbook
Author: Emmanuel Tsukerman
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-11-25
Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.
Python for Cybersecurity
This book provides a structured, hands-on introduction to using Python for cybersecurity. With the MITRE ATT&CK framework as a guide, readers will explore the lifecycle of a cyberattack and see how Python code can be used to solve key challenges at each stage of the process. Each application will be explored from the perspective of both the attacker and the defender, showing how Python can be used to automate attacks and to detect and prevent them. By following the MITRE ATT&CK framework, this book explores the use of Python for a number of cybersecurity uses cases, including: Intelligence collection Exploitation and lateral movement Persistence and privilege escalation Command and control Extraction and encryption of valuable data Each use case will include ready-to-run code samples and demonstrations of their use in a target environment. Readers will gain hands-on experience in applying Python to cybersecurity use cases and practice in creating and adapting Python code to address novel situations.