Everyone knows that AI and machine learning are the future of penetration testing. Large cybersecurity enterprises talk about hackers automating and smartening their tools; The newspapers report on cybercriminals utilizing voice transfer technology to impersonate CEOs; The media warns us about the implications of DeepFakes in politics and beyond...This book finally teaches you how to use Machine Learning for Penetration Testing.This book will be teaching you, in a hands-on and practical manner, how to use the Machine Learning to perform penetration testing attacks, and how to perform penetration testing attacks ON Machine Learning systems. It will teach you techniques that few hackers or security experts know about.You will learn- how to supercharge your vulnerability fuzzing using Machine Learning.- how to evade Machine Learning malware classifiers.- how to perform adversarial attacks on commercially-available Machine Learning as a Service models.- how to bypass CAPTCHAs using Machine Learning.- how to create Deepfakes.- how to poison, backdoor and steal Machine Learning models.And you will solidify your slick new skills in fun hands-on assignments.
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.
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Want Red Team offensive advice from the biggest cybersecurity names in the industry? Join our tribe. The Tribe of Hackers team is back with a new guide packed with insights from dozens of the world’s leading Red Team security specialists. With their deep knowledge of system vulnerabilities and innovative solutions for correcting security flaws, Red Team hackers are in high demand. Tribe of Hackers Red Team: Tribal Knowledge from the Best in Offensive Cybersecurity takes the valuable lessons and popular interview format from the original Tribe of Hackers and dives deeper into the world of Red Team security with expert perspectives on issues like penetration testing and ethical hacking. This unique guide includes inspiring interviews from influential security specialists, including David Kennedy, Rob Fuller, Jayson E. Street, and Georgia Weidman, who share their real-world learnings on everything from Red Team tools and tactics to careers and communication, presentation strategies, legal concerns, and more Learn what it takes to secure a Red Team job and to stand out from other candidates Discover how to hone your hacking skills while staying on the right side of the law Get tips for collaborating on documentation and reporting Explore ways to garner support from leadership on your security proposals Identify the most important control to prevent compromising your network Uncover the latest tools for Red Team offensive security Whether you’re new to Red Team security, an experienced practitioner, or ready to lead your own team, Tribe of Hackers Red Team has the real-world advice and practical guidance you need to advance your information security career and ready yourself for the Red Team offensive.
Your one-stop guide to learning and implementing Red Team tactics effectively Key FeaturesTarget a complex enterprise environment in a Red Team activityDetect threats and respond to them with a real-world cyber-attack simulationExplore advanced penetration testing tools and techniquesBook Description Red Teaming is used to enhance security by performing simulated attacks on an organization in order to detect network and system vulnerabilities. Hands-On Red Team Tactics starts with an overview of pentesting and Red Teaming, before giving you an introduction to few of the latest pentesting tools. We will then move on to exploring Metasploit and getting to grips with Armitage. Once you have studied the fundamentals, you will learn how to use Cobalt Strike and how to set up its team server. The book introduces some common lesser known techniques for pivoting and how to pivot over SSH, before using Cobalt Strike to pivot. This comprehensive guide demonstrates advanced methods of post-exploitation using Cobalt Strike and introduces you to Command and Control (C2) servers and redirectors. All this will help you achieve persistence using beacons and data exfiltration, and will also give you the chance to run through the methodology to use Red Team activity tools such as Empire during a Red Team activity on Active Directory and Domain Controller. In addition to this, you will explore maintaining persistent access, staying untraceable, and getting reverse connections over different C2 covert channels. By the end of this book, you will have learned about advanced penetration testing tools, techniques to get reverse shells over encrypted channels, and processes for post-exploitation. What you will learnGet started with red team engagements using lesser-known methodsExplore intermediate and advanced levels of post-exploitation techniquesGet acquainted with all the tools and frameworks included in the Metasploit frameworkDiscover the art of getting stealthy access to systems via Red TeamingUnderstand the concept of redirectors to add further anonymity to your C2Get to grips with different uncommon techniques for data exfiltrationWho this book is for Hands-On Red Team Tactics is for you if you are an IT professional, pentester, security consultant, or ethical hacker interested in the IT security domain and wants to go beyond Penetration Testing. Prior knowledge of penetration testing is beneficial.
In the rapidly evolving world of cybersecurity, the intersection of hacking and artificial intelligence (AI) has become an arena of immense potential. "Mastering Hacking with AI" by Kris Hermans is your comprehensive guide to harnessing the power of AI for ethical hacking purposes. This groundbreaking book takes you on a transformative journey, equipping you with the knowledge and skills to master the fusion of hacking and AI. Inside this groundbreaking book, you will: Explore the core principles of hacking and AI, including machine learning techniques, natural language processing, anomaly detection, and adversarial attacks, enabling you to develop advanced hacking strategies. Gain hands-on experience through real-world examples, step-by-step tutorials, and AI-driven tools, allowing you to apply AI techniques to identify vulnerabilities, automate penetration testing, and enhance threat intelligence. Understand the ethical implications of AI-driven hacking and learn how to responsibly use AI for cybersecurity purposes, adhering to legal and ethical frameworks. Stay ahead of the curve with discussions on emerging trends in AI and their impact on cybersecurity, such as AI-powered defences, deepfake detection, and autonomous threat hunting.
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data
Want Red Team offensive advice from the biggest cybersecurity names in the industry? Join our tribe. The Tribe of Hackers team is back with a new guide packed with insights from dozens of the world’s leading Red Team security specialists. With their deep knowledge of system vulnerabilities and innovative solutions for correcting security flaws, Red Team hackers are in high demand. Tribe of Hackers Red Team: Tribal Knowledge from the Best in Offensive Cybersecurity takes the valuable lessons and popular interview format from the original Tribe of Hackers and dives deeper into the world of Red Team security with expert perspectives on issues like penetration testing and ethical hacking. This unique guide includes inspiring interviews from influential security specialists, including David Kennedy, Rob Fuller, Jayson E. Street, and Georgia Weidman, who share their real-world learnings on everything from Red Team tools and tactics to careers and communication, presentation strategies, legal concerns, and more Learn what it takes to secure a Red Team job and to stand out from other candidates Discover how to hone your hacking skills while staying on the right side of the law Get tips for collaborating on documentation and reporting Explore ways to garner support from leadership on your security proposals Identify the most important control to prevent compromising your network Uncover the latest tools for Red Team offensive security Whether you’re new to Red Team security, an experienced practitioner, or ready to lead your own team, Tribe of Hackers Red Team has the real-world advice and practical guidance you need to advance your information security career and ready yourself for the Red Team offensive.
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
