Network Intrusion Detection System

Abstract

Intrusions in computing environments represent a significant and ongoing challenge in the realm of cybersecurity. As technology continues to evolve, so do the techniques used by malicious actors to exploit vulnerabilities in digital systems. Despite decades of security advancements, the increasing dependency on interconnected networks demands more intelligent and adaptive security solutions. This project addresses the growing need for proactive threat detection by developing a Machine Learning-based Intrusion Detection System (IDS) that leverages the Random Forest algorithm. Trained on a comprehensive dataset of network traffic, the system is capable of identifying complex patterns and accurately classifying various types of cyberattacks, including denial-of-service (DoS), probing, and unauthorized access attempts. By automating the detection process and enhancing accuracy through data-driven insights, this IDS provides an efficient, scalable, and robust layer of defense to protect critical computing infrastructure from evolving cyber threats.