Enhancing Network Security with a Hybrid Intrusion Detection System Using SVM

Saxena, Gaurav Kishor

doi:https://doi.org/10.55041/ijcope.v2i8.085

Volume 02, Issue 8

Published on: August 2026

ENHANCING NETWORK SECURITY WITH A HYBRID INTRUSION DETECTION SYSTEM USING SVM

Gaurav Kishor Saxena

Shambhu Dayal Sahu

CSE RKDF University

DOI:https://doi.org/10.55041/ijcope.v2i8.085

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Abstract

Networks are rapidly and inevitably becoming complex, also the number of cyber threats is rising. Technology that uses intrusion quote elimination methods is essential to most networked forms for intentional analysis. The paper provides a thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles. The method uses both abnormality detection, which is extremely useful for identifying novel and 0-day attacks, and misuse detection, which is more effective in the detection of known attack patterns. Support Vector Machines is applied in building the system that achieves excellent accuracy in classification of network traffic, and at the same time reducing false negative and false positive rates. The hybrid model is the approach through which data-driven analytics is performed on raw network traffic data to extract relevant features, which are then used at run-time for classification by use of SVM. Anomaly detection aims at discovering deviations originating from standard network behavior, whereas misuse detection employs aspects of known signatures attack. This anomaly or misuse based systems outperform state-of-the-art models with respect to accuracy 97.1 in all other parameters such as precision, recall, as well as the f1 score. Among the other positive aspects of this work is the possible use of SVM in hybrid intrusion detection frameworks foreseeing an all-encompassing solution to modern network security issues. The research will further consider utilization of adaptive learning in order to improve persistency of the system against learning to new threats.

Keywords:- Behavioral Anomaly Detection, Explainable Artificial Intelligence, Ensemble Learning, Anomaly Detection, Attack Classification

How to Cite this Paper

Saxena, G. K. (2026). Enhancing Network Security with a Hybrid Intrusion Detection System Using SVM. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.085

Saxena, Gaurav. "Enhancing Network Security with a Hybrid Intrusion Detection System Using SVM." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.085.

Saxena, Gaurav. "Enhancing Network Security with a Hybrid Intrusion Detection System Using SVM." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.085.

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References

Kim, S. Lee, and S. Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,” Expert Syst. Appl., vol. 41, no. 4, Part 2, pp. 1690–1700, 2014, doi: https://doi.org/10.1016/j.eswa.2013.08.066.

M. Maseno, Z. Wang, and H. Xing, “A Systematic Review on Hybrid Intrusion Detection System,” Secur. Commun. Networks, vol. 2022, 2022, doi: 10.1155/2022/9663052.

Omrani, A. Dallali, B. C. Rhaimi, and J. Fattahi, “Fusion of ANN and SVM classifiers for network attack detection,” 2017 18th Int. Conf. Sci. Tech. Autom. Control Comput. Eng. STA 2017 - Proc., vol. 2018-January, pp. 374–377, 2018, doi: 10.1109/STA.2017.8314974.

Kushal, B. Shanmugam, J. Sundaram, and S. Thennadil, “Self-healing hybrid intrusion detection system: an ensemble machine learning approach,” Discov. Artif. Intell., vol. 4, no. 1, p. 28, 2024, doi: 10.1007/s44163-024-00120-9.

Prithi and S. Sumathi, “Intrusion Detection System using Hybrid SVM-RF and SVM-DT in Wireless Sensor Networks,” no. 2, pp. 1926–1931, 2019, doi: 10.35940/ijrte.B1200.0882S819.

Alkasassbeh, “An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods,” J. Theor. Appl. Inf. Technol., vol. 95, no. 22, pp. 5962–5976, 2017.

Omrani, A. Dallali, B. C. Rhaimi, and J. Fattahi, “Fusion of ANN and SVM classifiers for network attack detection,” 2017 18th Int. Conf. Sci. Tech. Autom. Control Comput. Eng. STA 2017 - Proc., vol. 2018-January, pp. 374–377, 2018, doi: 10.1109/STA.2017.8314974.

Sedjelmaci and M. Feham, “Novel Hybrid Intrusion Detection System For Clustered Wireless Sensor Network,” Int. J. Netw. Secur. Its Appl., vol. 3, no. 4, pp. 1–14, 2011, doi: 10.5121/ijnsa.2011.3401.

A. R. Shah and B. Issac, “Performance comparison of intrusion detection systems and application of machine learning to Snort system,” Futur. Gener. Comput. Syst., vol. 80, pp. 157–170, 2018, doi: 10.1016/j.future.2017.10.016.

Yin, C.; Zhu, Y.; Fei, J.; He, X. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks. IEEE Access2017, 5, 21954–21961.

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•Published on: Aug 11 2026

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