A COMPREHENSIVE STUDY OF MACHINE LEARNING-BASED NETWORK ANOMALY DETECTION SYSTEMS

Abstract

The rapid growth of information technology has significantly increased the prevalence of cyber threats, making network security a critical priority for organizations worldwide. Network Intrusion Detection Systems (NIDS) play a vital role in safeguarding networks by identifying and responding to potential attacks. However, traditional rule-based approaches are often limited in their ability to cope with the complexity and constantly evolving nature of modern cyber threats. To address these challenges, the integration of Artificial Intelligence (AI) into NIDS has emerged as a promising solution due to its adaptability and capability to learn from dynamic data patterns. This paper presents a comprehensive review of AI-based techniques used in NIDS, including machine learning, deep learning, and hybrid approaches. It examines the advantages and limitations of these methods, evaluates their effectiveness in detecting various types of network attacks, and discusses current research trends and challenges in the field. Furthermore, the study emphasizes the significance of factors such as dataset selection, feature engineering, model design, and evaluation metrics in developing efficient AI-driven NIDS. By analyzing these components, the paper provides valuable insights into how AI can enhance the accuracy, scalability, and robustness of intrusion detection systems, thereby contributing to the advancement of cyber security.
Keywords: Intrusion, Detection, NIDS, Cyber, Threats.