Efficient Feature Selection for Intrusion Detection Systems with Priority Queue-Based GRASP

The Greedy Randomized Adaptive Search Procedure for Feature Selection (GRASP-FS) is a recently-proposed metaheuristic that optimizes the feature selection process for Intrusion Detection Systems (IDS) by combining exploration and refinement techniques for more assertive intrusion detection. However, GRASP-FS may be time and resource-consuming for large datasets. In this work, we propose GRASPQ-FS, an extended version of GRASP-FS using Priority Queues to reduce resource consumption and processing time. As an additional contribution, we provide a comprehensive analysis of the most suitable parameters for our GRASPQ-FS. Our results reveal that GRASPQ-FS can speed up feature selection up to 90% over GRASP-FS, without compromising F1-Score. Also, we observed that a priority queue with 50 solutions saved 50% in execution time while increasing the F1-Score by 4.5%.