On the Performance of GRASP-Based Feature Selection for CPS Intrusion Detection
| Autor: | Silvio E. Quincozes, Diego Passos, Daniel Mosse, Luiz Satoru Ochi, Vinícius F. dos Santos, Célio Albuquerque |
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| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science GRASP Feature selection Intrusion detection system Machine learning computer.software_genre Critical infrastructure Information system Local search (optimization) Artificial intelligence Electrical and Electronic Engineering business computer Metaheuristic Greedy randomized adaptive search procedure |
| Zdroj: | IEEE Transactions on Network and Service Management. 19:614-626 |
| ISSN: | 2373-7379 |
| DOI: | 10.1109/tnsm.2021.3088763 |
| Popis: | Cyber-Physical Systems (CPS) are the basis for the world’s critical infrastructure and, thus, have the potential to significantly impact human lives in the near future. In recent years, there has been an increasing demand for connectivity in CPS, which has brought to attention the issue of cybersecurity. Aside from traditional information systems threats, CPS face new challenges due to the heterogeneity of devices and protocols, as well as its strong reliability requirements. In this work, we provide a brief overview of the CPS architecture and applications and describe the security challenges in the three CPS layers of perception, transmission, and application. Besides, we discuss how feature selection (FS) may improve intrusion detection performance. In particular, we evaluate how metaheuristic approaches can improve classification performance in CPS perception, transmission, and application layers. Our results reveal that (i) Greedy Randomized Adaptive Search Procedure (GRASP) outperforms traditional filter-based methods, and (ii) using the proposed enhanced approaches in GRASP construction and local search phases can enhance the average F1-Score of five classifier algorithms. |
| Databáze: | OpenAIRE |
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