AccFIT-IDS: accuracy-based feature inclusion technique for intrusion detection system

In today’s digital landscape, maintaining security has become increasingly difficult. Many organizations implement security mechanisms to protect their valuable resources from potential threats. One such mechanism is the Intrusion Detection System (IDS), which examines network traffic to identify and prevent attack. However, the vast amount of network data requires an efficient processing approach. Among various studies from existing literature, Machine Learning (ML) algorithms provide a viable solution to detecting intrusion in voluminous data. ML based IDS still faces challenges, particularly due to high-dimensional data complexity, which affects the performance. To address this, the study proposed AccFIT (Accuracy-based Feature Inclusion Technique) for IDS, combining two-stage Feature Selection Algorithms (FSA). In stage 1, various filter-based feature selection methods are applied extract features from intial dataset. The derived features are concatenated and redundant features are excluded to produce an intermediary feature subset (Fintemediary). In stage 2, the Fintemediary is fed to a wrapper-based selection algorithm to derive an optimal subset Foptimal. Features are included or excluded based on their impact on model accuracy. The proposed AccFIT-IDS achieves 99.86% accuracy, with precision, recall and F1-score of 99.81%, 99.92% and 99.86%, respectively, and a false alarm rate of 0.0021, demonstrating its effectiveness.