Designing IDS to Analyze Malicious Attacks using Machine Learning

Abstract: This study presents a novel approach to enhance network security through the design and implementation of an Intrusion Detection System (IDS) employing machine learning (ML) techniques. The primary aim is to address current challenges in real-time threat detection by seamlessly integrating ML models, including Logistic Regression, Random Forest, and XGBoost. The research tackles drawbacks in existing systems, emphasizing the struggle with real-time threat detection, the complexity of ML integration. The working model classifies incoming requests as normal or intrusions, categorizing the latter into DDoS, R2L, U2R, or Probing attacks. Leveraging insights from four referenced papers, this study builds on efficient activation functions, optimized feature selection, and empirical analyses of ML models for adaptive network intrusion detection. Results showcase promising comparisons between ML algorithms which include Logistic Regression, Random Forest, XG Boost