GWO-Integrated Intrusion Classification using Artificial Neural Networks
Keywords:
Intrusion Detection, Grey Wolf Optimization (GWO), Artificial Neural Networks (ANN), Denial of Service (DOS), Cyber SecurityAbstract
Intrusion Detection System is a popular area of research to handle many forms of attacks in networks in the current linked world, where security is the primary concern. In accordance with network aspects, intrusion detection systems (IDS) identify dynamic and malicious network traffic. In the realm of cyber security, the rapid evolution of cyber threats necessitates advanced intrusion
detection systems. IDS has been developed in many forms using unique methodologies. One well-liked strategy is machine learning, which makes use of several algorithms like ANN, SVM, etc. But ANN is the technique that is most widely employed. Combining the ANN with different meta heuristic algorithms can dramatically increase its performance. In this context, this study introduces a novel approach by integrating Grey Wolf Optimization (GWO) with Artificial Neural Networks (ANN) for the purpose of intrusion classification. In order to categorize different sorts of attacks, such as denial of service KDD-99 data-set is used. The study demonstrates that ANN with GWO performs better than ANN, ANN with PSO, and ANN with GA. This research paper explores the development and functionalities of intrusion detection system, emphasizing its potential contribution to a safer network with the help of GWO and ANN with accuracy of 96%.
References
Gurbani Kaur, Dharmender Kumar, “Classification of Intrusion using Artificial Neural Network with GWO”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958 (Online), Volume-9 Issue-4, April 2020
Smith, J., Johnson, A.,"A Machine Learning Approach for Intrusion Detection and Classification”, Proceedings of the International Conference on Cyber security (ICCS), June 15-17, 2023,New York, USA
Kemmerer, Richard A., and Giovanni Vigna. "Intrusion detection: a brief history and overview." Computer 35, no. 4
(2002): supl27-supl30.
Wang, L., Zhang, S "Feature Selection and Ensemble Methods for Intrusion Classification", ACM Transactions on
Privacy and Security (TOPS), December 2023Chicago, USA
Chen, X., Liu, Y. "Intrusion Detection and Classification Using Genetic Algorithms and Decision Trees" International
Journal of Information Security, March 2024, Beijing, China
Zhang, Q., Li, W., "Intrusion Classification Using Quantum Walk Optimization", Proceedings of the International
Conference on Cyber security (ICCS) June 15-17, 2022 New York, USA.
Wang, H., Liu, Z. "Artificial Neural Networks for Intrusion Detection and Classification: A Comparative Study", IEEE
Transactions on Information Forensics and Security September 2023 New York, USA.
Chen, L., Wu, Y. Title: "Hybrid Intrusion Classification System Based on Quantum Walk Optimization and Artificial Neural Networks" ,International Journal of Computational Intelligence and Applications ,December 2023 ,Shanghai, China.
Gupta, S., Kumar, A. , "A Novel Approach for Intrusion Classification using Quantum Walk Optimization and Artificial Neural Networks" ,Proceedings of the International Conference on Machine Learning and Cyber security(ICMLCS), October 2022 , London, UK.
Hsiu, Pi-Cheng, Chin-Fu Kuo, Tei-Wei Kuo, and Eric YT Juan. "Scenario based threat detection and attack analysis." In
Security Technology, 2005. CCST'05. 39th Annual 2005 International Carnahan Conference on, pp. 279-282. IEEE, 2005.
Snapp, Steven R., James Brentano, Gihan V. Dias, Terrance L. Goan, L. Todd Heberlein, Che-Lin Ho, Karl N. Levitt et al. "DIDS (distributed intrusion detection system)-motivation, architecture, and an early prototype." In Proceedings of the 14th national computer security conference, vol. 1, pp. 167-176. 1991.
Cannady, James. "Artificial neural networks for misuse detection." In National information systems security
conference, vol. 26. 1998.
Garcia, M., Martinez, P.,"Deep Learning Techniques for Intrusion Detection and .Classification", IEEE Transactions
on Information Forensics and Security
September 2022, New York, USAEckmann, Steven T., Giovanni Vigna, and Richard A. Kemmerer. "STATL: An attack language for state-based intrusion detection." Journal of computer security 10, no. 1-2 (2002): 71-103.
Kim, J., Park, S., "Intrusion Classification with Quantum Walk Optimization-Enhanced Artificial Neural Networks”, Journal of Network and Computer Applications ,March 2024, Seoul, South Korea.
Mohammed, Muamer N., and Norrozila Sulaiman. "Intrusion detection system based on SVM for WLAN." Procedia
Technology 1 (2012): 313-317.
Jalil Kamarularifin Abd, Muhammad Hilmi Kamarudin, and Mohamad Noorman Masrek, "Comparison of machine
learning algorithms performance in detecting network intrusion," In Networking and Information Technology (ICNIT), 2010 International Conference on, pp. 221-226, IEEE,2010.
