International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Implement an Intrusion Detection System Utilizing Machine Learning and Principal Component Analysis

Abstract

The ever-evolving cybersecurity sector requires robust intrusion detection systems (IDS). Traditional rules-based measures are no longer sufficient due to the complexity of cyber threats, requiring new approaches. This study presents the architecture of an intrusion detection system combining machine learning and principal component analysis (PCA) to increase network security. A network traffic classification system was built and tested on the NSL-KDD dataset and used PCA for dimensionality reduction. The results were cross-validated to reduce overfitting and ensure generalizability of the model. Low-variance precision refers to the consistency of the cross-validation fold. The combination of PCA and machine learning models exceeds previous studies with an F1 score for the random forest model of over 99%. The study improves intrusion detection and network protection against cyber-attacks.

Country : Lebanon

1 Rula Abdulwahid Mohammed2 Youssef A. Bazzi

  1. Faculty of Engineering, Islamic University of Lebanon, Wardanieh, Lebanon
  2. Department of Electrical and Computer Engineering, Lebanese University, Beirut, Lebanon

IRJIET, Volume 8, Issue 2, February 2024 pp. 1-7

doi.org/10.47001/IRJIET/2024.802001

Full Paper
Download

References

  1. D. Kapil, N. Mehra, A. Gupta, S. Maurya, and A. Sharma, "Network security: threat model, attacks, and IDS using machine learning," in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 203-208, 2021.
  2. A. D. Jadhav, "Two Phase-Intrusion Detection System (TP-IDS) model using Machine Learning Techniques," Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 9, pp. 417-425, 2021.
  3. J. Kevric, S. Jukic, and A. Subasi, "An effective combining classifier approach using tree algorithms for network intrusion detection," Neural Computing and Applications, vol. 28, Suppl 1, pp. 1051-1058, 2017.
  4. B. Wen and G. Chen, "Principal component analysis of network security data based on projection pursuit," in International Conference on Network Computing and Information Security, Berlin, Heidelberg: Springer Berlin Heidelberg,pp. 380-387, 2012.
  5. K. K. Vasan and B. Surendiran, "Dimensionality reduction using principal component analysis for network intrusion detection," Perspectives in Science, vol. 8, pp. 510-512, 2016.
  6. T. T. Khoei, G. Aissou, W. C. Hu, and N. Kaabouch, "Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid," in Proc. 2021 IEEE International Conference on Electro Information Technology (EIT), Mt. Pleasant, MI, USA, pp. 129-135, 2021.
  7. T. T. Khoei, S. Ismail, and N. Kaabouch, "Boosting-based Models with Tree-structured Parzen Estimator Optimization to Detect Intrusion Attacks on Smart Grid," in Proc. 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0165-0170, 2021.
  8. [8] Z. E. Mrabet, H. E. Ghazi, and N. Kaabouch, "A performance comparison of data mining algorithms-based intrusion detection system for smart grid," in Conference on Electro Information Technology (EIT), IEEE, Piscataway, NJ, USA,pp. 298-303, 2019.
  9. E. Anthi, L. Williams, M. Słowińska, G. Theodorakopoulos, and P. Burnap, "A supervised intrusion detection system for smart home IoT devices," Internet of Things Journal, vol. 6, pp. 9042-9053, 2019.
  10. R. Yao, N. Wang, Z. Liu, P. Chen, and X. Sheng, "Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach," Sensors, vol. 21, 626, 2021.
  11. H. Yang and F. Wang, "Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network," IEEE Access, vol. 7, pp. 64366-64374, 2019.
  12. Y. Wang, Z. Zhang, J. Ma, and Q. Jin, "KFRNN: An Effective False Data Injection Attack Detection in Smart Grid Based on Kalman Filter and Recurrent Neural Network," IEEE Internet of Things Journal, vol. 9, pp. 6893-6904, 2022.
  13. S. Majidi, S. Hadayeghparast, and H. Karimipour, "FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid," International Journal of Critical Infrastructure Protection, vol. 37, 100508, 2022.
  14. S. Ahmed, Y. Lee, S. Hyun, and I. Koo, "Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest," IEEE Transactions on Information Security, vol. 14, pp. 2765-2777, 2019.
  15. D. M. Menon and N. Radhika, "Anomaly detection in smart grid traffic data for home area network," in Proc. 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, India, pp. 1-4, 2016.
  16. P. R. Grammatikis, P. Sarigiannidis, G. Efstathopoulos, and E. Panaousis, "ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid," Sensors, vol. 20, 5305, 2020.
  17. H. Karimipour, A. Dehghantanha, R. M. Parizi, K. R. Choo, and H. Leung, "A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids," IEEE Access, vol. 7, pp. 80778-80788, 2019.
  18. A. Barua, D. Muthirayan, P. P. Khargonekar, and M. A. Al Faruque, "Hierarchical Temporal Memory Based Machine Learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract," in Proc. ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), Sydney, Australia, pp. 188-189, 2020.
  19. W. Xu, J. Jang-Jaccard, A. Singh, Y. Wei, and F. Sabrina, "Improving performance of autoencoder-based network anomaly detection on NSL-KDD dataset," IEEE Access, vol. 9, pp. 140136-140146, 2021.
  20. T. Su, H. Sun, J. Zhu, S. Wang, and Y. Li, "BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset," IEEE Access, vol. 8, pp. 29575-29585, 2020.
  21. S. Farhat, M. Abdelkader, A. Meddeb-Makhlouf, and F. Zarai, "Comparative study of classification algorithms for cloud IDS using NSL-KDD dataset in WEKA," in 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 445-450, 2020.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies