Network Investigation of Cyber Threat Using Machine Learning

Network security has become increasingly important with the rapid expansion of interconnected digital systems and the growing sophistication of cyber threats. Among the major network-based threats, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), and Address Resolution Protocol (ARP) spoofing attacks pose significant risks to data confidentiality, system availability, and secure communication. The dynamic nature of these attacks presents considerable challenges to conventional security mechanisms, which often rely on predefined rules and signatures. To address these limitations, this research investigates the application of machine learning techniques for intelligent network threat detection. The study focuses on identifying anomalous network behavior, distinguishing legitimate traffic from malicious activities, and improving the reliability and accuracy of cyber threat detection. The proposed machine learning-driven approach aims to strengthen intrusion detection capabilities and support more adaptive and effective network security mechanisms against evolving cyber threats.