A Case Study on Cyber Attack Detection Using Machine Learning: IDS for Detecting Cyber Attacks Using Machine Learning

This chapter focusses on leveraging machine learning (ML) for cyber-attack prevention, addressing a range of threats and assaults. Machine learning is a crucial component of modern cybersecurity, offering a flexible approach to defend information systems against the constantly evolving tactics of malicious actors. By training both supervised and unsupervised ML algorithms on diverse datasets, we tackle issues such as hostile assaults and class imbalance. A key aspect of our work is prioritizing the interpretability of ML models to effectively manage and reduce false positives and false negatives. Additionally, we explore the challenges of integrating ML findings with existing cybersecurity frameworks, aiming for seamless collaboration between traditional security measures and ML-driven solutions. Our goal is to provide valuable insights on utilizing ML to prevent cyberattacks, highlighting its benefits, limitations, and future potential. Ultimately, we aim to enhance cybersecurity defenses in dynamic threat landscapes by clarifying the role of ML in cybersecurity.