Botnet is a combination of the two terms bot and net. The bot part represents the fact that this malware automates things and tasks like a robot. The second part refers to a network, in other words, a network of compromised devices. So, by definition, a botnet is a form of malware that attacks computers on the internet and controls them with command and control servers to perform a wide variety of automated tasks, including sending spam emails and performing Distributed Denial of Service (DDoS) attacks. Attacked machines join an immense network of compromised machines. One of the most notable botnets in previous years was the Mirai botnet. Mirai means the future in Japanese. This botnet hit millions of online devices, especially Internet of Things (IoT) appliances, by scanning and identifying vulnerable machines, taking advantage of the fact that most of them are...
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Mastering Machine Learning for Penetration Testing
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Mastering Machine Learning for Penetration Testing
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Overview of this book
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.
Table of Contents (13 chapters)
Preface
Free Chapter
Introduction to Machine Learning in Pentesting
Phishing Domain Detection
Malware Detection with API Calls and PE Headers
Malware Detection with Deep Learning
Botnet Detection with Machine Learning
Machine Learning in Anomaly Detection Systems
Detecting Advanced Persistent Threats
Evading Intrusion Detection Systems
Bypassing Machine Learning Malware Detectors
Best Practices for Machine Learning and Feature Engineering
Assessments
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