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Advisor(s)
Abstract(s)
The 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%.
Description
Keywords
Intrusion Detection System (IDS) Feature Selection GRASP Cyber-Physical System (CPS)
Citation
Quincozes 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/CloudNet62863.2024.10815746
Publisher
IEEE