Enhanced Feature Selection With EFPA for Ensemble Intrusion Detection
Pemilihan Fitur yang Ditingkatkan dengan EFPA untuk Deteksi Intrusi Ensemble
DOI:
https://doi.org/10.21070/pels.v8i1.2673Keywords:
EFPA, Ensemble Classifier, Intrusion Detection, NSL-KDD, UNSW-NB15Abstract
Background: The proliferation of IoT devices increases network exposure to sophisticated attacks such as DDoS, demanding robust intrusion detection. Specific Background: Traditional ML-based IDS face challenges with high-dimensional data and evolving attack patterns. Knowledge Gap: There is a need for automated feature selection that preserves detection performance while reducing complexity for large modern datasets. Aim: This study proposes an Enhanced Flower Pollination Algorithm (EFPA) for optimal feature selection combined with an ensemble classifier (Random Forest, ID3, SVM) to improve IoT intrusion detection. Methods: The model was evaluated on NSL-KDD and UNSW-NB15 with preprocessing, SMOTE balancing, and 70:30 train–test splits. Results: The EFPA-selected features with ensemble voting achieved 99.67% accuracy on UNSW-NB15 and 99.32% on NSL-KDD. Novelty: Integration of EFPA for dimensionality reduction with ensemble classification on modern benchmarks. Implications: The approach reduces computational load while maintaining high detection performance, suggesting promise for scalable IDS in IoT environments.
Highlights:
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EFPA reduces feature set while preserving detection accuracy.
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Ensemble voting improves generalization across benchmarks.
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High accuracy achieved on UNSW-NB15 and NSL-KDD.
Keywords: EFPA, Ensemble Classifier, Intrusion Detection, NSL-KDD, UNSW-NB15
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References
[1] J. Healey, “The US Government and Zero-Day Vulnerabilities: From Pre-Heartbleed to Shadow Brokers,” Journal of International Affairs, vol. 1, pp. 1–15, 2016.
[2] D. O’Brien, “Internet Security Threat Report—Ransomware 2017,” Symantec, vol. 11, pp. 203–214, 2017.
[3] P. Anantharaman et al., “Going Dark: A Retrospective on the North American Blackout,” in Proc. New Security Paradigms Workshop (NSPW), 2018, pp. 1–10.
[4] J. Hawdon et al., “Cybercrime Victimization Among Virginia Businesses: Frequency, Vulnerabilities, and Consequences of Cybervictimization,” Criminal Justice Studies, pp. 1–23, 2023.
[5] H. Abdi and L. J. Williams, “Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression,” in Computational Toxicology, vol. II, New York, NY: Springer, 2013, pp. 549–579.
[6] P. Garcia-Teodoro, J. Diaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-Based Network Intrusion Detection: Techniques, Systems and Challenges,” Computers & Security, vol. 28, no. 1–2, pp. 18–28, 2009.
[7] S. Mukkamala, G. Janoski, and A. Sung, “Intrusion Detection Using Neural Networks and Support Vector Machines,” in Proc. Int. Joint Conf. Neural Netw. (IJCNN’02), 2002, pp. 1702–1707.
[8] D. E. Denning, “An Intrusion-Detection Model,” IEEE Transactions on Software Engineering, vol. SE-13, no. 2, pp. 222–232, 1987.
[9] E. Vasilomanolakis, S. Karuppayah, M. Mühlhäuser, and M. Fischer, “Taxonomy and Survey of Collaborative Intrusion Detection,” ACM Computing Surveys, vol. 47, no. 4, pp. 1–33, 2015.
[10] A. H. Sung and S. Mukkamala, “Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks,” in Proc. Symp. Applications and the Internet (SAINT’03), 2003, pp. 209–216.
[11] M. A. Siddiqi, W. Pak, and M. A. Siddiqi, “A Study on the Psychology of Social Engineering-Based Cyberattacks and Existing Countermeasures,” Applied Sciences, vol. 12, no. 12, p. 6042, 2022.
[12] O. I. Falowo et al., “Threat Actors’ Tenacity to Disrupt: Examination of Major Cyberattacks,” Journal of Cybersecurity, vol. 9, no. 1, pp. 1–12, 2023.
[13] S. Roy et al., “A Lightweight Supervised Intrusion Detection Mechanism for IoT Networks,” Future Generation Computer Systems, vol. 127, pp. 276–285, 2022.
[14] A. Ponmalar and V. Dhanakoti, “An Intrusion Detection Approach Using Ensemble Support Vector Machine Based Chaos Game Optimization Algorithm in Big Data Platform,” Applied Soft Computing, vol. 116, p. 108295, 2022.
[15] T. Verwoerd and R. Hunt, “Intrusion Detection Techniques and Approaches,” Computer Communications, vol. 25, no. 15, pp. 1356–1365, 2002.
[16] P. Kabiri and A. A. Ghorbani, “Research on Intrusion Detection and Response: A Survey,” International Journal of Network Security, vol. 1, no. 2, pp. 84–102, 2005.
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