Next-Generation Network Intrusion Detection: Leveraging Deep Learning Techniques
Keywords:
Security of computer networks, Intrusion Detection Systems, advanced machine learning, streamlined data encoding, specific network intrusion dataset.Abstract
The Network Intrusion Detection System (NIDS) plays a pivotal role as an indispensable tool for system administrators in the discernment and detection of network security breaches within their respective organizations. However, the advancement of a multifaceted and exceedingly proficient Network Intrusion Detection System (NIDS) is not devoid of its proportionate set of obstacles, particularly when confronted with unanticipated and capricious cyber assaults. We propose a ground-breaking and cutting-edge deep learning paradigm as the underpinning for the creation of an efficient and adaptable Network Intrusion Detection System (NIDS).The present study utilizes the Self Learning (STL) methodology, a sophisticated DL technique, for the purpose of analyzing the NSL-KDD dataset. This particular dataset is widely recognized as a benchmark in the field of network intrusion analyzing. In this study, we present the performance evaluation of the proposed methodology and conduct a comparative analysis with a range of previous research endeavors. The metrics being considered encompass a range of quantitative measures, including accuracy, precision, recall, and f-measure values.
Downloads
References
S. Shaukat et al., "Intrusion Detection and Attack Classification Leveraging Machine Learning Technique," 2020 14th International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates, 2020, pp. 198-202, doi: 10.1109/IIT50501.2020.9299093.
K. Sood et al., "Performance Evaluation of a Novel Intrusion Detection System in Next Generation Networks," in IEEE Transactions on Network and Service Management, vol. 20, no. 3, pp. 3831-3847, Sept. 2023, doi: 10.1109/TNSM.2023.3242270.
A. Selamnia, B. Brik, S. M. Senouci, A. Boualouache and S. Hossain, "Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 2080-2085, doi: 10.1109/GLOBECOM48099.2022.10001675.
H. Alavizadeh, H. Alavizadeh, and J. Jang-Jaccard, “Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection,” Computers, vol. 11, no. 3, p. 41, Mar. 2022, doi: 10.3390/computers11030041. [Online]. Available: http://dx.doi.org/10.3390/computers11030041
S. BOUKRIA and M. GUERROUMI, "Intrusion detection system for SDN network using deep learning approach," 2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), Skikda, Algeria, 2019, pp. 1-6, doi: 10.1109/ICTAACS48474.2019.8988138.
Dalal, S., Lilhore, U.K., Faujdar, N. et al. Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree. J Cloud Comp 12, 137 (2023). https://doi.org/10.1186/s13677-023-00517-4
L. T. Le and T. N. Thinh, "On the Improvement of Machine Learning Based Intrusion Detection System for SDN Networks," 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam, 2021, pp. 464-469, doi: 10.1109/NICS54270.2021.9701522.
G. Rathinavel, N. Muralidhar, N. Ramakrishnan and T. O'Shea, "Efficient Generative Wireless Anomaly Detection for Next Generation Networks," MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), Rockville, MD, USA, 2022, pp. 594-599, doi: 10.1109/MILCOM55135.2022.10017520.
E. Gyamfi and A. Jurcut, “Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets,” Sensors, vol. 22, no. 10, p. 3744, May 2022, doi: 10.3390/s22103744. [Online]. Available: http://dx.doi.org/10.3390/s22103744
A. Le, P. Dinh, H. Le and N. C. Tran, "Flexible Network-Based Intrusion Detection and Prevention System on Software-Defined Networks," 2015 International Conference on Advanced Computing and Applications (ACOMP), Ho Chi Minh City, Vietnam, 2015, pp. 106-111, doi: 10.1109/ACOMP.2015.19.
S. Mehta, V. Kukreja and A. Gupta, "Next-Generation Wheat Disease Monitoring: Leveraging Federated Convolutional Neural Networks for Severity Estimation," 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-6, doi: 10.1109/INCET57972.2023.10169991.
D. Han et al., "Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2632-2647, Aug. 2021, doi: 10.1109/JSAC.2021.3087242.
H. Dong, A. Munir, H. Tout and Y. Ganjali, "Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities," in IEEE Access, vol. 9, pp. 136459-136475, 2021, doi: 10.1109/ACCESS.2021.3117763.
S. Lin et al., "Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks," 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC), Atlanta, GA, USA, 2022, pp. 16-25, doi: 10.1109/CIC56439.2022.00013.
M. T. Khan, A. Akhunzada and S. Zeadally, "Proactive Defense for Fog-to-Things Critical Infrastructure," in IEEE Communications Magazine, vol. 60, no. 12, pp. 44-49, December 2022, doi: 10.1109/MCOM.005.2100992.
K. Sood, M. R. Nosouhi, D. D. N. Nguyen, F. Jiang, M. Chowdhury and R. Doss, "Intrusion Detection Scheme With Dimensionality Reduction in Next Generation Networks," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 965-979, 2023, doi: 10.1109/TIFS.2022.3233777.
G. -x. Yao et al., "Research and Implementation of Next Generation Network Intrusion Detection System Based on Protocol Analysis," 2008 ISECS International Colloquium on Computing, Communication, Control, and Management, Guangzhou, China, 2008, pp. 353-357, doi: 10.1109/CCCM.2008.30.
W. Jo, S. Kim, C. Lee, and T. Shon, “Packet Preprocessing in CNN-Based Network Intrusion Detection System,” Electronics, vol. 9, no. 7, p. 1151, Jul. 2020, doi: 10.3390/electronics9071151. [Online]. Available: http://dx.doi.org/10.3390/electronics9071151
Dhanwanth, B. ., Vivek, B. ., Abirami, M. ., Waseem, S. M. ., & Manikantaa, C. . (2023). Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 336–344. https://doi.org/10.17762/ijritcc.v11i5s.7035
Vivek, B. ., Nandhan, S. H. ., Zean, J. R. ., Lakshmi, D. ., & Dhanwanth, B. . (2023). Applying Machine Learning to the Detection of Credit Card Fraud. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 643–652. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3267
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
