Intrusion detection system using artificial intelligence and machine learning

Abstract

In Cyber security, the safeguarding of digital assets against malicious intrusions stands as a paramount concern. This report explores the development and evaluation of an Intrusion Detection System (IDS) leveraging a machine learning paradigm of Neural Networks. With Cyber security threats evolving in complexity and severity, the imperative for robust intrusion detection mechanisms has never been more pressing. Drawing from a diverse array of network traffic data, our IDS endeavors to discern subtle anomalies indicative of potential security breaches. Through meticulous training and evaluation processes, we scrutinize the efficacy of Random Forest and Neural Network algorithms in detecting and mitigating these threats. The report offers a comprehensive examination of the methodologies employed in model development, feature engineering, and performance evaluation. Comparative analyses highlight the strengths and limitations of each approach, shedding light on their respective contributions to intrusion detection accuracy and efficiency. By leveraging insights gleaned from extensive experimentation and evaluation, this research aims to inform the ongoing refinement of IDS technologies, paving the way for more resilient Cyber security frameworks in an ever-evolving threat landscape