Hybrid Feature Selection with Adaptive Threshold Optimization for Efficient Machine Learning–based Intrusion Detection
TL;DR
This study proposes a novel IDS framework with hybrid feature selection method and adaptive threshold optimization, and shows that this solution considerably increases IDS efficiency and decreases false positive rates.
Abstract
With the increasing use of computer networks and internet services, network security becomes crucially important. The existing intrusion detection methods are based on signature analysis or rules, but these methods are inefficient against zero-day attacks and evolving cyber threats. In order to overcome the disadvantages of existing solutions, this study proposes a novel IDS framework with hybrid feature selection method and adaptive threshold optimization. The IDS under consideration uses the UNSW-NB15 dataset. First, the data pre-processing algorithms such as encoding, scaling and class balancing are used. Hybrid feature selection is then implemented by means of a combination of chi-squared filtering and Particle Swarm Optimization (PSO). Several supervised learning algorithms, including K-Nearest Neighbors, Decision Tree, Logistic Regression and Random Forest are used for training and testing. Moreover, Adaptive Threshold Testing Algorithm (ATTA) is applied for dynamic optimization of decision thresholds. The results show that this solution considerably increases IDS efficiency and decreases false positive rates.