Cyberattack Detection Using Machine Learning for Enhanced Network Security

Abstract

It employs machine learning to enhance network security and identify breaches in this research. In contemporary communication networks, this will identify and prevent aggressive behavior. In order to analyze network traffic patterns and detect anomalies caused by malware intrusions, DOS attacks, phishing, or unauthorized access, the research employs supervised and unsupervised machine learning algorithms, including Random Forest, Decision Trees, Support Vector Machines, and Neural Networks. The proposed approach identifies both normal and aberrant activities by examining packet flow, connection length, source and destination IP behavior, and protocol consumption. Traditional security systems that rely on signatures for threat detection are outperformed by machine learning-driven detection models in terms of real-time attack detection, false alarms, and threat detection speed. The investigation determined that enhanced cybersecurity is required to safeguard personal data, networks, and online communication.