Advanced Feature Selection Techniques for Machine Learning-Based Detection of Encrypted Malicious Traffic

The increasing prevalence of encrypted traffic in modern networks presents significant challenges in detecting malicious activities, necessitating advanced techniques for effective security monitoring. This book chapter explores the integration of machine learning (ML) for encrypted malicious traffic detection, focusing on innovative feature selection methods. It delves into various techniques, including filter, wrapper, and embedded methods, evaluating their strengths, limitations, and application in network security. The chapter emphasizes the importance of feature extraction, representation, and selection in improving the accuracy of machine learning models while handling encrypted data. It discusses the unique challenges posed by encrypted traffic and how ML models, particularly supervised and unsupervised learning approaches, can address these issues. By comparing traditional detection methods with machine learning-driven solutions, this work highlights the potential of ML to enhance security measures in encrypted environments. The findings provide a roadmap for future research in the field of network traffic analysis and cybersecurity.