A Comprehensive Framework for Enhancing Intrusion Detection Systems through Advanced Analytical Techniques
الموضوعات : Pattern RecognitionChetan Gupta 1 , Amit Kumar 2 , Neelesh Kumar Jain 3
1 - Jaypee University of Engineering and Technology, Guna, India
2 - Jaypee University of Engineering and Technology, Guna, India
3 - Jaypee University of Engineering and Technology, Guna, India
الکلمات المفتاحية: IDS, DOS, XGBOOST, PCA, HIDS, NIDS,
ملخص المقالة :
Intrusion detection systems (IDS) are security technologies that monitor system activity, network traffic, and settings to detect potential threats. IDS provide proactive security management, detecting anomalies and ensuring continuous monitoring. It protects critical assets, such as sensitive data and intellectual property, from unauthorized access or data breaches, preventing downtime and disruption to business operations. In this paper we present a hybrid model based on Principal Component Analysis (PCA) and XGBoost algorithms. To show the effectiveness of the proposed system, various parameters are evaluated on the standard NSL-KDD dataset. First we trained the model using trained dataset and then evaluate the performance the model using testing dataset. In proposed work the we store the data into two-dimensional structure then we standardized and take a most significance features of the data then calculate the covariance matrix, after that calculate the eigenvalues and eigenvectors of the matrix and short in the descending order and using principal component identify the new features and remove the insignificant features. The proposed model outperforms and produces 97.76% accuracy and 94.51% precision; the recall rate is 93.44% and 93.97% F1-Score, which is much better than the previous proposed models. This hybrid approach is better to handle the categorical data and able to find the pattern well and the outcome of the model clearly shows the effectiveness of the proposed system.
1. Louati, F., Ktata, F.B. et al. “Big-IDS: a decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks”. – In: Cluster Computing, March 2024, Volume 27, pages 6823–6841. https://doi.org/10.1007/s10586-024-04306-9.
2. Soltani, M., Khajavi, K., Jafari Siavoshani, M. et al. “A multi-agent adaptive deep learning framework for online intrusion detection”. – In: Cyber security, May 2024 Volume 7, article number 9. https://doi.org/10.1186/s42400-023-00199-0.
3. Gupta, N., Jindal, V. et al. “A Survey on Intrusion Detection and Prevention Systems”. – In: SN Computer Science. SCI. June 2023, Volume 4, article number 439. https://doi.org/10.1007/s42979-023-01926-7.
4. Talukder, M.A., Islam, M.M., Uddin, M.A. et al. “Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction”. – In: J Big Data, February 2024, Volume 11, article number 33. https://doi.org/10.1186/s40537-024-00886-w.
5. AL-Syouf, R., Bani-Hani, R. & AL-Jarrah, O.Y. “Machine learning approaches to intrusion detection in unmanned aerial vehicles (UAVs). – In: Neural Computing & Application”, August 2024 Volume 36, pages 18009–18041. https://doi.org/10.1007/s00521-024-10306-y.
6. Kumar, V., Kumar, V., Singh, N. et al. “P3IDF-EC: PCA-Based Privacy-Preserving Intrusion Detection Framework for Edge Computing”. – In: SN COMPUT. SCI. August 2024. Volume 5. https://doi.org/10.1007/s42979-024-03152-1.
7. Behiry, M.H., Aly, M. “Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods”. – In: J Big Data, January 2024, volume 11. https://doi.org/10.1186/s40537-023-00870-w.
8. Altamimi, S., Abu Al-Haija, Q. “Maximizing intrusion detection efficiency for IoT networks using extreme learning machine”. – In: Discover Internet Things, July 2024, volume 4. https://doi.org/10.1007/s43926-024-00060-x.
9. N. Chothani, “Combined PCA and Kernel-Based Extreme Learning Machine Technique for Classification of Faults in IEEE 9- Bus System”. – In: Third International Conference on Power, Control and Computing Technologies (ICPC2T). January 2024, pp.380-385, https://doi.org/10.1109/ICPC2T60072.2024.10474888.
10. Ajmal, S., Ashfaq, R.A.R., Raza, A. et al. “IDS-FRNN: an intrusion detection system with optimized fuzziness-based sample selection technique”. – In: Neural Computing & Applications. September 2024. https://doi.org/10.1007/s00521-024-10333-9.
11. Patthi, S., Singh, S. et al. “2-layer classification model with correlated common feature selection for intrusion detection system in networks”. – In: Multimedia Tools and Applications January 2024 Volume 83, pages 61213–61238. https://doi.org/10.1007/s11042-023-17781-w.
12. Al-Haija Qasem A, Saleh E et al. “Detecting port scan attacks using logistic regression”. – In: 4th International symposium on advanced electrical and communication technologies (ISAECT), pages 1–5. IEEE. https://doi.org/10.1109/ISAECT53699.2021.9668562.
13. Zaben, S.O. “IDC-insight: boosting intrusion detection accuracy in IoT networks with Naïve Bayes and multiple classifiers”. – In: International Journal of Information Technology June 2024. https://doi.org/10.1007/s41870-024-02026-2.
14. Al-Haija Qasem A, McCurry Charles D, et al. “Intelligent self-reliant cyber-attacks detection and classification system for IOT communication using deep convolutional neural network”. – In: Selected papers from the 12th international networking conference: INC 2020 12, pages 100–116. Springer.
15. Saurabh, K., Sharma, V., Singh, U. et al. ”HMS-IDS: Threat Intelligence Integration for Zero-Day Exploits and Advanced Persistent Threats in IoT”. – In: Arabian Journal for Science and Engineering, July 2024. https://doi.org/10.1007/s13369-024-08935-5.
16. Thakkar, A. et al. “A Review on Challenges and Future Research Directions for Machine Learning-Based Intrusion Detection System”. – In: Archives of Computational Methods in Engineering May 2023 Volume 30, pages 4245–4269. https://doi.org/10.1007/s11831-023-09943-8.
17. Roshan, K. et al. Ensemble adaptive online machine learning in data stream: a case study in cyber intrusion detection system. – In: International Journal of Information Technology, February 2024. https://doi.org/10.1007/s41870-024-01727-y.
18. Najafli, S., Toroghi Haghighat, A. et al. “A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing”. – In: The Journal of Supercomputing, August 2024, Volume 80, pages 26088–26110. https://doi.org/10.1007/s11227-024-06417-x.
19. Wang, K., Li, J. & Wu, W. “A novel transfer extreme learning machine from multiple sources for intrusion detection”. – In: Peer-to-Peer Networking and Applications. October 2024, Volume 17, pages 33–47. https://doi.org/10.1007/s12083-023-01569-8.
20. Ngo, VD. Vuong, TC, Van Luong, T. et al. “Machine learning-based intrusion detection feature selection versus feature extraction”. – In: Cluster Computing, July 2024, Volume 27, pages 2365–2379. https://doi.org/10.1007/s10586-023-04089-5.
21. Mustafa, Z., Amin, R., Aldabbas, H. et al. “Intrusion detection systems for software-defined networks: a comprehensive study on machine learning-based techniques”. – In: Cluster Computing, April 2024Volume 27, pages 9635–9661. https://doi.org/10.1007/s10586-024-04430-6.
22. Madhuri, S., Lakshmi, S.V. “A machine learning-based normalized fuzzy subset linked model in networks for intrusion detection”. – In: Soft Computing. May 2023. https://doi.org/10.1007/s00500-023-08160-6.
23. Sajid, M., Malik, K.R., Almogren, A. et al. “Enhancing intrusion detection: a hybrid machine and deep learning approach”. – In: Journal of Cloud Computing, July 2024, Volume 13, article number 123. https://doi.org/10.1186/s13677-024-00685-x.
24. Liu, Y., Zhang, K. & Wang, Z. “Intrusion detection of manifold regularized broad learning system based on LU decomposition”. – In: The Journal of Supercomputing, June 2023 Volume 79, pages 20600–20648. https://doi.org/10.1007/s11227-023-05403-z.
25. Qi, H., Liu, X., Gani, A. et al. “Quantum particle Swarm optimized extreme learning machine for intrusion detection”. – In: The Journal of Supercomputing, March 2024, Volume 80, pages 14622–14644. https://doi.org/10.1007/s11227-024-06022-y.
26. Wang, X., Dai, L. & Yang, G. “A network intrusion detection system based on deep learning in the IoT”. – In: The Journal of Supercomputing July 2024, Volume 80, pages 24520–24558. https://doi.org/10.1007/s11227-024-06345-w.
27. Merzouk, M.A., Neal, C., Delas, J. et al. “Adversarial robustness of deep reinforcement learning-based intrusion detection”. – In: International Journal of Information Security August 2024 Volume 23, pages 3625–3651. https://doi.org/10.1007/s10207-024-00903-2.
28. Maseno, E.M., Wang, Z. “Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection”. – In: Journal of Big Data, February 2024, Volume 11, article number 24, https://doi.org/10.1186/s40537-024-00887-9.
29. Hamdi, N. “Federated learning-based intrusion detection system for Internet of Things”. – In: International Journal of Information Security. July 2023, Volume 22, pages 1937–1948. https://doi.org/10.1007/s10207-023-00727-6.
30. Akhtar, M.A., Qadri, S.M.O., Siddiqui, M.A. et al. “Robust genetic machine learning ensemble model for intrusion detection in network traffic”. – In: Scientific Reports. October 2023, Volume 13, article number 17227. https://doi.org/10.1038/s41598-023-43816-1.
31. Talukder, M.A., Islam, M.M., Uddin, M.A. et al. “Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction”. – In: Journal of Big Data. February 2024, Volume 11, article number 33. https://doi.org/10.1186/s40537-024-00886-w.
32. Chavan, P. Hanumanthappa, H., Satish, E.G. et al. “Enhanced Hybrid Intrusion Detection System with Attention Mechanism using Deep Learning”. – In: SN Computer Science. SCI. May 2024 Volume 5, article number 534. https://doi.org/10.1007/s42979-024-02852-y.
33. Rumen Ketipov, Roman Schnalle, Lyubka Doukovska, Dustin Dehez. “Managing Cyber security: Digital Footprint Threats”. – In: Cybernetics and Information Technologies 2024, Volume 24, No 3. https://doi.org/10.2478/cait-2024-0030.
34. Praveen, Kumar Kaithal, Varsha, Sharma. “African Vulture Optimization-Based Decision Tree (AVO-DT): An Innovative Method for Malware Identification and Evaluation through the Application of Meta-Heuristic Optimization Algorithm”. – In: Cybernetics and Information Technologies 2024, Volume 24, Issue 2. https://doi.org/10.2478/cait-2024-0020.
35. Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri, Babu Poorva, Pamidi Sai Ushasree, et al. “Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models”. – In: Cybernetics and Information Technologies. Sep. 2021. Volume 24, Issue 2. https://doi.org/10.2478/cait-2021-0037.
36. Seshu, Bhavani Mallampati, and Hari, Seetha. “Enhancing Intrusion Detection with Explainable AI: A Transparent Approach to Network Security”. – In: Cybernetics and Information Technologies. Dec. 2023. Volume 24, Issue 2. https://doi.org/10.2478/cait-2024-0006.
37. Sarwat Ejaz, Umara Noor and Zahid Rashid. “Visualizing Interesting Patterns in Cyber Threat Intelligence Using Machine Learning Techniques”. – In: Cybernetics and Information Technologies. June 2022. Volume 22, Issue 2. https://doi.org/10.2478/cait-2022-0019.