The previous section was a real-world implementation of MLP networks for detecting malware. Now, we are going to explore other artificial network architectures and we are also going to learn how to use one of them to help malware analysts and information security professionals to detect and classify malicious code. Before diving into the technical details and the steps for the practical implementation of the DL method, it is essential to learn and discover the other different architectures of artificial neural networks. We discussed some of them briefly in Chapter 1, Introduction to Machine Learning in Pentesting. The major artificial neural networks are discussed now.
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.
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Current Title:
Mastering Machine Learning for Penetration Testing
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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Customer Reviews