Quincozes, Vagner E.Quincozes, Silvio E.Albuquerque, CélioPassos, DiegoMassé, Daniel2025-01-072025-01-072024-12-31Quincozes V. E., Quincozes S. E., Albuquerque C., Passos D., Massé D.. Efficient Feature Selection for Intrusion Detection Systems with Priority Queue-Based GRASP, 2024 IEEE 13th International Conference on Cloud Networking (CloudNet), Rio de Janeiro, Brazil, 2024, pp. 1-8, doi: 10.1109/CloudNet62863.2024.10815746979-8-3503-7656-2979-8-3503-7657-92374-3239http://hdl.handle.net/10400.21/18126The Greedy Randomized Adaptive Search Proce dure 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 RASPQ-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%.engIntrusion Detection System (IDS)Feature SelectionGRASPCyber-Physical System (CPS)conference object10.1109/CloudNet62863.2024.108157462771-5663