Journal Press India^®

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The escalating sophistication and frequency of cyber-attacks have necessitated the development of more advanced intrusion detection systems (IDS). This research presents the development of an innovative IDS employing Analysis of Variance (ANOVA) for feature selection and a Support Vector Machine (SVM) algorithm for intrusion detection. The aim is to enhance detection accuracy while reducing computational overhead. ANOVA was used to identify significant features from vast and complex network traffic data, simplifying the high-dimensional data and improving the detection system’s efficiency. The selected features are then classified using the Radial Basis Function (RBF) SVM algorithm, renowned for its high accuracy and robustness in handling high-dimensional data. A comparative analysis with existing IDS models demonstrates the improved efficiency and accuracy of the proposed model. This work provides an advanced methodology for cyber security, contributing to the ever-evolving battle against cyber threats.

Keywords

Intrusion detection; Machine learning; Feature selection; Support vector machine; ANOVA

1. Abdulsalam S. O., Ayofe R. A., Edafeajiroke M. F., Ajao J. F. & Babatunde R. S. (2024). Development of an intrusion detection system using mayfly feature selection and artificial neural network algorithms. LAUT ECH Journal of Engineering and Technology, 18(2), 148. Retrieved from Doi: 16010.36108/laujet/4202.81.0241
2. Alagrash, Y. H., Mehdy, H. S. & Mahdi, R. H. (2023). A review of intrusion detection system methods and Techniques: Past, present and future. International Journal on Technical and Physical Problems of Engineering (IJTPE), 15(1).
3. Chatterjee, S., Shaw, V., & Das, R. (2023). Multi-stage intrusion detection system aided by Grey Wolf optimization algorithm. Research Square (Research Square). Retrieved from https://doi.org/10.21203/rs.3.rs-2680915/v1
4. Faizin, M. A., Kurniasari D. T.,  Elqolby, N., Putra, M. A. R., & Ahmad,  T. (2024). Optimizing feature selection method in intrusion detection system using thresholding. (2024b). International Journal of Intelligent Engineering and Systems, 17(3), 214-226. Retrieved from https://doi.org/10.22266/ijies2024.0630. 18
5. Megantara, A., & Ahmad, T. (2021). ANOVA-SVM for selecting subset features in encrypted internet traffic classification. International Journal of Intelligent Engineering and Systems, 14(2), 536 546. Retrieved from https://doi.org/ 10.22266/ijies2021.0430.48
6. Mohammadi, M., Rashid, T.A., Karim, S.H., Aldalwie, A.H.M., Tho, Q.T., Bidaki, M., Rahmani, A. M., & Hosseinzadeh, M. (2021). A comprehensive survey and taxonomy of  the SVM-based intrusion detection systems. Journal of Network and Computer Applications, 178, 102983. Retrieved from https://doi.org/10.1016 /j.jnca.2021.102983
7. Musthafa, M. B., Huda, S., Kodera, Y., Ali, M. A., Araki, S., Mwaura, J., & Nogami, Y. (2024). Optimizing IoT intrusion detection using balanced class distribution, feature selection, and ensemble machine learning techniques. Sensors, 24(13), 4293. Retrieved from https://doi.org/10.3390/s24134293
8. Ogundokun, R. O., Awotunde, J. B., Sadiku, P., Adeniyi, E. A., Abiodun, M., & Dauda, O. I. (2021). An enhanced intrusion detection system using particle swarm optimization feature extraction technique. Procedia Computer Science, 193, 504–512. Retrieved from https://doi.org/10.1016/j.procs.2021.10.052
9. Sarumi, O. A., Adetunmbi, A. O., & Adetoye, F. A. (2020). Discovering computer networks intrusion using data analytics and machine intelligence. Scientific African, 9, e00500. Retrieved from https://doi.org/10.1016/j.sciaf.2020.e00500
10. Shakeela, S., Shankar, N. S., Reddy, P. M., Tulasi, T. K., & Koneru, M. M. (2020). Optimal ensemble learning based on distinctive feature selection byunivariate ANOVA F statistics for IDS. Intern ational Journal of Electronics and Telecommunications, 267275. Retrieved from https://doi.org/10.24425/ijet.20 21.135975
11. Stiawan, D., Heryanto, A., Bardadi, A., Rini, D. P., Subroto, I. M. I., Kurniabudi, N., Idris, M. Y. B., Abdullah, A. H., Kerim, B., & Budiarto, R. (2021). An approach for optimizing ensemble intrusion detection systems. IEEE Access, 9, 6930–6947. Retrieved from https://doi.org/10.1109/access.2020.3046246
12. Vapnik, V.N. (1998). Statistical Learning Theory. New York, USA: John Wiley & Sons.
13. Zeinalpour, A., & McElroy, C. P. (2024). Comparing metaheuristic search techniques in addressing the effectiveness of ClusteringBased DDOS attack detection methods. Electronics, 13(5), 899. Retrieved from https://doi.org/10.3390/ electronics13050899