MACHINE LEARNING TECHNIQUES FOR CYBER ATTACKS DETECTION

Abstract

Cyber attacks have become increasingly sophisticated and frequent, posing significant threats to information systems, communication networks, and digital infrastructures worldwide. Traditional security mechanisms, including signature-based and rule-based detection systems, often struggle to identify unknown and evolving attack patterns. Machine learning techniques provide an intelligent and adaptive approach for detecting cyber attacks by analyzing large volumes of network traffic and system data to recognize malicious activities. This paper investigates the application of machine learning techniques for cyber attack detection, focusing on the identification of threats such as malware infections, phishing attacks, network intrusions, ransomware, and denial-of-service attacks. The proposed framework utilizes data preprocessing, feature extraction, and machine learning algorithms to classify normal and malicious activities with high accuracy. By learning hidden patterns and behavioral characteristics from historical data, machine learning models can effectively detect both known and previously unseen attacks. Experimental results demonstrate that machine learningbased detection systems significantly improve threat identification, reduce false positives, and enhance overall cybersecurity performance. The proposed approach offers a scalable and efficient solution for protecting modern network environments against continuously evolving cyber threats.