MACHINE LEARNING TECHNIQUES FOR CYBER ATTACKS DETECTION

Abstract

Cyber attacks have become increasingly sophisticated, making traditional security methods less effective at identifying malicious activities. Machine learning (ML) techniques offer powerful solutions for detecting and preventing these attacks by automatically analyzing large amounts of network and system data. This paper reviews various ML approaches—including supervised, unsupervised, and reinforcement learning—for identifying threats such as malware, intrusion attempts, phishing, and anomalous behavior. It highlights how ML models can learn patterns of normal and abnormal activities, improve detection accuracy, and reduce false alarms. Challenges such as data quality, model interpretability, and evolving attack strategies are also discussed. Overall, machine learning provides an adaptive and efficient framework for strengthening cybersecurity systems