What do you think?
Rate this book
Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python
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 algorithmsBook DescriptionCyber 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 learnTake an in-depth look at machine learningGet to know natural language processing (NLP)Understand malware feature engineeringBuild generative adversarial networks using Python librariesWork on threat hunting with machine learning and the ELK stackExplore the best practices for machine learningWho this book is forThis 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.
Table of ContentsIntroduction to Machine Learning in PentestingPhishing Domain DetectionMalware Detection with API Calls and PE HeadersMalware Detection with Deep LearningBotnet Detection with Machine LearningMachine Learning in Anomaly Detection SystemsDetecting Advanced Persistent ThreatsEvading Intrusion Detection Systems with Adversarial Machine LearningBypass machine learning malware DetectorsBest Practices for Machine Learning and Feature EngineeringAssessments
Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithmsBook DescriptionCyber 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 learnTake an in-depth look at machine learningGet to know natural language processing (NLP)Understand malware feature engineeringBuild generative adversarial networks using Python librariesWork on threat hunting with machine learning and the ELK stackExplore the best practices for machine learningWho this book is forThis 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.
Table of ContentsIntroduction to Machine Learning in PentestingPhishing Domain DetectionMalware Detection with API Calls and PE HeadersMalware Detection with Deep LearningBotnet Detection with Machine LearningMachine Learning in Anomaly Detection SystemsDetecting Advanced Persistent ThreatsEvading Intrusion Detection Systems with Adversarial Machine LearningBypass machine learning malware DetectorsBest Practices for Machine Learning and Feature EngineeringAssessments
478 pages, Kindle Edition
Published June 27, 2018
13 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
May 1, 2022
At this point, I do not particularly advocate, this approach- perhaps this will change with changes like neurosymbolic efforts, combining DNN with symbolic capabilities. Right now, the consensus, is it is hard to latch onto patterns in code, that make malware easy to recognize. (This is a review for the Japanese edition.)
Displaying 1 of 1 review