Many different deep network architectures were proposed by machine learning practitioners and malware analysts to detect both known and unknown malware; some of the proposed architectures include restricted Boltzmann machines and hybrid methods. You can check some of them in the Further reading section. Novel approaches to detect malware and malicious software show many promising results. However, there are many challenges that malware analysts face when it comes to detecting malware using deep learning networks, especially when analyzing PE files because to analyze a PE file, we take each byte as an input unit, so we deal with classifying sequences with millions of steps, in addition to the need of keeping complicated spatial correlation across functions due to function calls and jump commands.
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Mastering Machine Learning for Penetration Testing
By :
Mastering Machine Learning for Penetration Testing
By:
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