Analysing the Role of Machine Learning Models in Threat Detection and Prevention in Computer Systems

Abstract

The rapid evolution of cyber threats necessitates adaptive defense mechanisms beyond traditional rule-based methods. Machine learning (ML) has emerged as a critical tool in cybersecurity, offering robust solutions for detecting anomalies, predicting threats, and automating responses. This paper examines the application of ML models, including supervised, unsupervised, and reinforcement learning, in intrusion detection, malware analysis, and phishing prevention. It highlights the strengths of algorithms like Support Vector Machines, Random Forests, and Neural Networks while addressing challenges such as the need for labeled data, computational demands, and adversarial vulnerabilities. The study underscores the effectiveness of hybrid approaches and the potential of emerging technologies like explainable AI and federated learning in enhancing ML-based cybersecurity. Comparative analysis reveals trade-offs in model performance, scalability, and resource requirements, with Random Forests and Neural Networks emerging as versatile options for robust threat detection. This research emphasizes the transformative potential of ML in creating proactive and resilient cybersecurity systems capable of countering sophisticated attacks.