USING MACHINE LEARNING TO ANALYSE MALICIOUS ATTACKS IN IDS DESIGN
International Research Journal of Modernization in Engineering Technology and Science · 2024
This paper offers a new method for improving network security by using machine learning (ML) techniques in the design and implementation of an intrusion detection system (IDS).The primary objective is to address current challenges associated with real-time threat detection by seamlessly integrating ML models such as Logistic Regression, Random Forest, and XGBoost.The research focuses on mitigating shortcomings in existing systems, particularly the difficulties in achieving real-time threat detection and the complexities involved in integrating ML technologies.Incoming requests are categorized by the working model as either normal or incursions, with the latter being further divided into DDoS, R2L, U2R, and probing attacks.This study improves on effective activation functions, improved feature selection, and empirical assessments of ML models for adaptive network intrusion detection by utilizing knowledge from four referenced studies.The results indicate encouraging comparisons between ML algorithms, such as XG Boost, Random Forest, and Logistic Regression.