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A Comprehensive Review of Intrusion detection by leveraging the Machine Learning Techniques

Aakansha Patel

With advancement in digital devices and openness of critical network systems. The threat of cyber-attack and intrusion attack is a major concern to the critical resources of network security and systems. The cyber attacker takes the advantages of weakness and vulnerabilities exist in network system or users devices in order to exploit the various assets and steal valuable information. Over the past decades, Internet and computer systems have raised numerous security issues due to the explosive use of networks. Intrusion Detection System (IDS) provides the protection against any kind of network attack by detecting network intrusions from the suspicious traffic data. However, most of the intrusion detection data suffers from high dimensionality, due to this IDS leads to the degraded performance and lower prediction rate of any kind of new intrusion. Therefore, this work presents a comprehensive analysis of an intrusion detection system for network traffic data using machine learning techniques. The proposed system employed machine learning and data mining techniques in order to detect different kind of intrusion from network traffic on NSL-KDD dataset. The obtained results of proposed work show that the intrusion detection model for network system can efficiently and effectively identify intrusion behavior and malicious intension with higher accuracy.