Exploring Software Vulnerabilities with Fuzz Testing and Machine Learning

This chapter focuses on software fuzz testing to uncover vulnerabilities by analyzing code paths through the use of genetic algorithms and machine learning models like GANs and LSTMs. It emphasizes the integration of advanced techniques for detecting insider threats and malware while discussing the challenges of maintaining robust machine learning models. The critical need for effective anomaly detection in cybersecurity and the importance of balancing detection efficiency with human analyst capabilities are also highlighted.