AI-DRIVEN INTRUSION DETECTION SYSTEMS: LEVERAGING MACHINE LEARNING FOR REAL-TIME CYBER THREAT IDENTIFICATION AND MITIGATION

Abstract

This study compares and contrasts conventional intrusion detection system (IDS) techniques with the increasing role of AI-driven IDS to cope with the new dynamics of cyber threats. The nature of cyberattacks is constantly evolving, and they are turning out to be more sophisticated in nature, thus making traditional security architectures unable to keep up with the speed and complexity of newer attacks. Strengthening IDS can be achieved by integrating artificial intelligence, i.e., machine learning (ML) and deep learning (DL) models. Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and the highly developed CNN-LSTM model are a few of the AI techniques discussed in this study. The best performance among them is illustrated by CNN-LSTM, which is characterized by excellent accuracy, precision, recall, and universality. According to the study, AI-driven IDS is far superior to its conventional counterparts as it is capable of detecting threats with greater accuracy, fewer false positives, enable faster response rates, and accommodate new, unfamiliar cyber threats. The research indicates that AI-based IDS possesses a revolutionary approach to threat minimization as well as enhancing real-time cybersecurity defense, forecasting a future where cyber threats are managed and governed better in advanced, dynamic environments.