Enhancing Network Security with BGOTSVM: A New Approach to Intrusion Detection
Keywords:
Cyber Security, Threat Detection, Artificial Intelligence, Machine Learning, Explainable AI.Abstract
The increasing complexity of ensuring cybersecurity has become a significant challenge due to the rapid growth of computer connectivity and the proliferation of applications reliant on computer networks. With the rise of cyber threats, there is a critical need for robust defense mechanisms to detect and mitigate potential risks. One promising approach lies in the development of Intrusion Detection Systems (IDS), which are designed to identify anomalies and security breaches in computer networks. This research introduces a novel Binary Grasshopper Optimized Twin Support Vector Machine (BGOTSVM) security model, which integrates machine learning (ML) techniques for intrusion detection. By ranking security features based on their relevance and selecting the most significant features, the model reduces the feature dimensionality, enhancing the predictive performance and reducing computational costs. The proposed model is compared with four common ML techniques—Decision Tree (DT), Random Decision Forest (RDF), Random Tree (RT), and Artificial Neural Network (ANN)—to evaluate its efficacy. Experimental results demonstrate that the BGOTSVM model outperforms conventional ML techniques, offering a promising solution for real-world network intrusion detection.
Downloads
References
A. L. Bellovin, “Reflections on Firewalls and Security Monitoring: An Overview,” IEEE Communications Magazine, vol. 32, no. 4, pp. 40-48, April 1994.
D. B. Johnson and M. R. Johnson, “Detecting and Mitigating Distributed Denial of Service Attacks,” IEEE Security & Privacy, vol. 5, no. 3, pp. 46-53, May-June 2007.
K. Bertino, B. Sandhu, and C. F. Hof, “Database Security: Concepts, Approaches, and Challenges,” IEEE Transactions on Dependable and Secure Computing, vol. 3, no. 1, pp. 2-19, Feb.-Mar. 2006.
S. Bhosale and A. P. Bhave, “Intrusion Detection System: A Survey,” IEEE International Conference on Computing, Communication, and Automation (ICCCA), pp. 512-517, 2010.
Sivananda Reddy Julakanti. (2021). Implementing Spark Data Frames for Advanced Data Analysis. International Journal of Intelligent Systems and Applications in Engineering, 9(1), 62–66. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7086
Julakanti, S. R., Sattiraju, N. S. K., & Julakanti, R. (2022). Transforming Data in SAP HANA: From Raw Data to Actionable Insights. NeuroQuantology, 19(11), 854-861. Retrieved from https://www.neuroquantology.com/open-access/Transforming+Data+in+SAP+HANA%253A+From+Raw+Data+to+Actionable+Insights_14495/
Julakanti, S. R., Sattiraju, N. S. K., & Julakanti, R. (2021). Creating high-performance data workflows with Hadoop components. NeuroQuantology, 19(11), 1097–1105. Retrieved from https://www.neuroquantology.com/open-access/Creating+High-Performance+Data+Workflows+with+Hadoop+Components_14496/
Sivananda Reddy Julakanti, Naga Satya Kiranmayee Sattiraju, & Rajeswari Julakanti. (2023). Data Protection through Governance Frameworks. Journal of Computational Analysis and Applications (JoCAAA), 31(1), 158–162. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/1525
Sivananda Reddy Julakanti. (2021). Optimizing Storage Formats for Data Warehousing Efficiency. International Journal on Recent and Innovation Trends in Computing and Communication, 9(5), 71–78. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/11291
Sivananda Reddy Julakanti, Naga Satya Kiranmayee Sattiraju, Rajeswari Julakanti. (2022). Security by Design: Integrating Governance into Data Systems. International Journal of Communication Networks and Information Security (IJCNIS), 14(2), 393–399. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/7756
Julakanti, S. R., Sattiraju, N. S. K., & Julakanti, R. (2022). Governance Meets Security Safeguarding Data and Systems. NeuroQuantology, 20(7), 4847-4855. Retrieved from https://www.neuroquantology.com/open-access/Governance+Meets+Security+Safeguarding+Data+and+Systems_14526/
Julakanti, S. R., Sattiraju, N. S. K., & Julakanti, R. (2022). Incremental Load and Dedup Techniques in Hadoop Data Warehouses. NeuroQuantology, 20(5), 5626-5636. Retrieved from https://www.neuroquantology.com/open-access/Incremental+Load+and+Dedup+Techniques+in+Hadoop+Data+Warehouses_14518/
Julakanti, S. R., Sattiraju, N. S. K., & Julakanti, R. (2022). Securing the Cloud: Strategies for Data and Application Protection. NeuroQuantology, 20(9), 8062–8073. Retrieved from https://www.neuroquantology.com/open-access/Securing+the+Cloud%253A+Strategies+for+Data+and+Application+Protection_14532/
Julakanti, S. R., Sattiraju, N. S. K., & Julakanti, R. (2022). Multi-Cloud Security: Strategies for Managing Hybrid Environments. NeuroQuantology, 20(11), 10063–10074. Retrieved from https://www.neuroquantology.com/open-access/Multi-Cloud+Security%253A+Strategies+for+Managing+Hybrid+Environments_14543/
M. E. J. Newman, Networks: An Introduction. Oxford University Press, 2010.
R. Sommer and V. Paxson, “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection,” IEEE Symposium on Security and Privacy, pp. 305-316, 2010.
C. Papernot, N. McDaniel, and I. Goodfellow, “Transferability in Machine Learning: From Phenomena to Black-Box Attacks using Adversarial Samples,” IEEE Symposium on Security and Privacy, pp. 222-238, 2016.
Reddy Julakanti, S. (2023). AI Techniques to Counter Information Security Attacks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5), 518–527. https://doi.org/10.17762/ijritcc.v11i5.11368
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, May 2015.
T. M. Mitchell, Machine Learning. McGraw-Hill, 1997.
M. T. Ribeiro, S. Singh, and C. Guestrin, ““Why Should I Trust You?” Explaining the Predictions of Any Classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135-1144, 2016.
A. L. Barrington, “Implementing Network Security,” IEEE Security & Privacy, vol. 8, no. 4, pp. 58-63, July-August 2010.
F. Doshi-Velez and B. Kim, “Towards a Rigorous Science of Interpretable Machine Learning,” arXiv preprint arXiv:1702.08608, 2017.
C. Szegedy et al., “Intriguing Properties of Neural Networks,” arXiv preprint arXiv:1312.6199, 2013.
J. Kurose and K. Ross, Computer Networking: A Top-Down Approach. Pearson, 2017.
S. W. McShane, “A Review of Intrusion Detection Systems: Classification, Challenges and Opportunities,” IEEE Access, vol. 3, pp. 152-163, 2015.
B. Preneel, “AI-Based Intrusion Detection Systems,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 7, pp. 1405-1418, July 2015.
R. C. Martin, “The Principles of Object-Oriented Design,” IEEE Software, vol. 13, no. 6, pp. 50-57, Nov.-Dec. 1996.
M. E. O’Neil and P. M. Friendly, “Statistical Analysis and Data Mining: Methods for Studying Large Data Sets,” IEEE Computational Intelligence Magazine, vol. 1, no. 3, pp. 55-63, Sept. 2006.
A. Ghosh and M. Reiter, “Using Data Mining to Improve Intrusion Detection,” Proceedings of the IEEE International Conference on Data Mining, pp. 108-115, 1998.
S. S. Zargar, M. Joshi, and N. Tipper, “A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 2046-2069, Fourth Quarter 2014.
P. A. Diniz and G. S. J. Costa, “An Overview of the Current Trends in Intrusion Detection,” IEEE Communications Surveys & Tutorials, vol. 13, no. 1, pp. 1-19, First Quarter 2011.
E. Bertino and P. Sandhu, “Database Security: Concepts, Approaches, and Challenges,” IEEE Transactions on Dependable and Secure Computing, vol. 1, no. 1, pp. 2-19, Jan.-Mar. 2004.
A. K. Jain, M. N. Murty, and P. J. Flynn, Data Clustering: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 1-18, January 2000.
T. J. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
D. Hand, Statistics and Data Analysis for Financial Engineering. Springer, 2009.
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.
