PERFORMANCE ANALYSIS OF AN EFFICIENT FRAMEWORK FOR INTRUSION DETECTION SYSTEM USING DATA MINING TECHNIQUES

Abstract

In the midst of the COVID-19 epidemic crisis, due to the tremendous development of mobile and internet technologies, the excessive growth in cyber-crime makes networksurity a major concern. As a result, individuals and companies are gradually moving towards the use of Intrusion Detection System (IDS), as it plays a persuasive role in monitoring and detecting the traffic of a network. However, high dimensional data affect the performance of IDS by reducing prediction accuracy, increasing false positive rate and classification time. Hence the focus of this research work is to develop a novel framework by integrating Auto – Bi Level (ABL) Classification with Double Filtering Fine Tuning – Ensemble Hybrid (DFFT-EH) feature selection. The experiments are conducted using NSL- KDD a benchmark intrusion detection dataset and it is proved that the proposed framework performs well with good accuracy, less false positive rate and less classification time when compared with voting ensemble classifier and other existing standard algorithms.

Keywords

• auto – bi level (abl) classification
• intrusion detection system (ids)
• data mining (dm)
• feature selection