Machine Learning-based Intrusion Detection System for Social Network Infrastructure

Abstract

The growing number of cyber-attacks demands a critical measure to prevent unauthorized data access. Thus, intrusion detection has become critical to deal with such attacks. This work attempts to identify malicious connections using a few key parameters. The system has been trained using data relating to normal and abnormal events through machine learning and data mining techniques. To detect intrusions, this study assessed five distinct machine learning models: Random Forest, Bagging, Boosting, Support Vector Machine, and K-Nearest Neighbor (KNN). Based on the number of features, iterations, and hyperparameters, the models were evaluated using experimental data collected in real time. With a detection rate of up to 98.7%, the Random Forest approach surpassed existing machine learning models for intrusion detection. The paper proposes a novel intrusion detection system (IDS) based on these findings that successfully identifies possible threats before they seriously compromise network security and stop cyberattacks.