Cybersecurity Threat Detection using Machine Learning and Network Analysis

Abstract

Cybercriminals continually develop innovative strategies to confound and frustrate their victims, necessitating constant vigilance to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) has emerged as a powerful technique for intelligent cyber analysis, enabling proactive defenses by studying recurring patterns of successful attacks. However, two significant drawbacks hinder the widespread adoption of ML in security analysis: high computing overheads and the need for specialized frameworks. This study aims to quantify the extent to which a hub can enhance ecosystem safety. Typical cyberattacks were executed on an Internet of Things (IoT) network within a smart house to validate the hub's efficacy. Furthermore, the resistance of the intrusion detection system (IDS) to adversarial machine learning (AML) attacks was investigated, where models are targeted with adversarial samples exploiting weaknesses in the pre-trained detector.