Intelligent Network Traffic Anomaly Detection Using ML Algorithms

The rapid advancement of internet technologies, cloud computing, and Internet of Things (IoT) devices has led to a significant increase in the volume and complexity of network traffic. As digital systems become more interconnected, they also become more vulnerable to various forms of cyberattacks. Malicious activities such as denial-of-service attacks, probing attempts, and unauthorized access have become increasingly sophisticated, posing serious threats to modern network infrastructures. Conventional network security mechanisms, including firewalls and signature-based intrusion detection systems (IDS), are primarily designed to detect known attack patterns and therefore struggle to identify new or evolving attack strategies. Traditional security solutions often rely on predefined rules or signatures, which limits their ability to respond effectively to previously unseen threats. As attackers continuously modify their techniques to evade detection, there is a growing need for intelligent security systems capable of identifying abnormal patterns in network traffic. Anomaly-based intrusion detection systems address this challenge by employing data-driven approaches that learn the characteristics of normal and malicious network behavior directly from historical data. By analyzing traffic patterns at the connection level, machine learning models can identify unusual deviations that may indicate potential intrusions, even in the absence of known attack signatures.