NETWORK SECURITY ENHANCEMENT THROUGH MACHINE LEARNING-BASED ATTACK DETECTION

Abstract

This paper talks about a way to improve network security through intelligent threat identification that is based on machine learning. Cyberattacks are becoming more complex and changing at a rapid pace, making it difficult for traditional rule-based security solutions to keep up. In order to identify legitimate and malicious actions in real-time, the suggested system makes use of state-of-the-art machine learning techniques to examine network traffic patterns. Packet size, protocol behavior, and traffic flow characteristics are just a few of the many features that are gathered in order to train classification models. Attacks such as denial-of-service (DoS), infiltration attempts, and unauthorized access are all detectable by the system. Experimental results show improved detection accuracy, reduced false positives, and quicker reaction times when compared to earlier techniques. By allowing adaptive learning and continual development, the suggested methodology greatly enhances network defensive mechanisms and offers a scalable answer to contemporary cybersecurity challenges.