What this book covers
Chapter 1, Introduction to Machine Learning in Pentesting, introduces reader to the fundamental concepts of the different machine learning models and algorithms, in addition to learning how to evaluate them. It then shows us how to prepare a machine learning development environment using many data science Python libraries.
Chapter 2, Phishing Domain Detection, guides us on how to build machine learning models to detect phishing emails and spam attempts using different algorithms and natural language processing (NLP).
Chapter 3, Malware Detection with API Calls and PE Headers, explains the different approaches to analyzing malware and malicious software, and later introduces us to some different techniques for building a machine learning-based malware detector.
Chapter 4, Malware Detection with Deep Learning, extends what we learned in the previous chapter to explore how to build artificial neural networks and deep learning to detect malware.
Chapter 5, Botnet Detection with Machine Learning, demonstrates how to build a botnet detector using the previously discussed techniques and publicly available botnet traffic datasets.
Chapter 6, Machine Learning in Anomaly Detection Systems, introduces us to the most important terminologies in anomaly detection and guides us to build machine learning anomaly detection systems.
Chapter 7, Detecting Advanced Persistent Threats, shows us how to build a fully working real-world threat hunting platform using the ELK stack, which is already loaded by machine learning capabilities.
Chapter 8, Evading Intrusion Detection Systems with Adversarial Machine Learning, demonstrates how to bypass machine learning systems using adversarial learning and studies some real-world cases, including bypassing next-generation intrusion detection systems.
Chapter 9, Bypass Machine Learning Malware Detectors, teaches us how to bypass machine learning-based malware detectors with adversarial learning and generative adversarial networks.
Chapter 10, Best Practices for Machine Learning and Feature Engineering, explores different feature engineering techniques, in addition to introducing readers to machine learning best practices to build reliable systems.