Implementation of Machine Learning-Based Data Mining Techniques for IDS

Abstract

The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract user behaviour patterns from network traffic data. Association rules and sequence rules are the main foundations of data mining used for intrusion detection. Given the Auto encoder algorithm's traditional method's bottleneck of frequent itemsets mining, we provide a Length-Decreasing Support to Identify Intrusion based on Data Mining, which is an upgraded Data Mining Techniques based on Machine Learning for IDS. Based on test results, it appears that the suggested strategy is successful