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Weighted ANOVA and Mutual Information for Enhanced Intrusion Detection System
Corresponding Author(s) : I Gede Teguh Satya Dharma
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 1, February 2026 (Article in Progress)
Abstract
The rapid escalation in the sophistication of network attacks has exposed the limitations of traditional Intrusion Detection Systems (IDS). While machine learning has shown great promise in enhancing IDS performance, its success often hinges on the effectiveness of feature selection. Standard feature selection techniques, however, struggle in cybersecurity applications due to the highly imbalanced nature of network traffic datasets. In such settings, minority attack classes, though critical, are often overshadowed by majority classes, leading to reduced detection of rare intrusions. To address this challenge, we propose a hybrid feature selection framework that integrates Analysis of Variance (ANOVA) and Mutual Information (MI) with a novel class-frequency weighting mechanism. This weighting scheme adjusts the relevance score of each feature according to the distribution of classes, ensuring that features associated with rare attacks are more strongly emphasized during the selection process. We evaluate our method on the UNSW-NB15 dataset using a Support Vector Machine classifier. The results show that our approach achieves substantial gains in recall for underrepresented classes while simultaneously reducing feature dimensionality and maintaining efficiency. By improving the visibility of features tied to minority attacks, the proposed framework provides a more balanced and reliable solution for modern IDS. This contribution advances the detection of rare but impactful threats and highlights a scalable pathway for building more resilient cybersecurity defenses.
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- N. Challa, “Unveiling the Shadows: A Comprehensive Exploration of Advanced Persistent Threats (APTs) and Silent Intrusions in Cybersecurity,” Journal of Artificial Intelligence & Cloud Computing, pp. 1–5, Dec. 2022, doi: 10.47363/JAICC/2022(1)190.
- I. A. Kandhro et al., “Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures,” IEEE Access, vol. 11, pp. 9136–9148, 2023, doi: 10.1109/ACCESS.2023.3238664.
- B. Alotaibi, “A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities,” Sensors, vol. 23, no. 17, p. 7470, Aug. 2023, doi: 10.3390/s23177470.
- N. Singh, R. Buyya, and H. Kim, “Securing Cloud-Based Internet of Things: Challenges and Mitigations,” Sensors, vol. 25, no. 1, p. 79, Dec. 2024, doi: 10.3390/s25010079.
- H. A. Hassan, E. E. Hemdan, W. El-Shafai, M. Shokair, and F. E. A. El-Samie, “Intrusion Detection Systems for the Internet of Thing: A Survey Study,” Wirel Pers Commun, vol. 128, no. 4, pp. 2753–2778, Feb. 2023, doi: 10.1007/s11277-022-10069-6.
- Z. Dai et al., “An intrusion detection model to detect zero-day attacks in unseen data using machine learning,” PLoS One, vol. 19, no. 9, Sep. 2024, doi: 10.1371/journal.pone.0308469.
- K. Bonagiri, P. Krishnamoorthy, V. Keerthiga, D. Kirubakaran, R. David, and B. Nancharaiah, “Cybersecurity With Machine Learning: Implementing AI Algorithms for Intrusion Prevention, Advanced Data Protection, and Real-Time Threat Analysis,” in 2025 International Conference on Computational, Communication and Information Technology (ICCCIT), IEEE, Feb. 2025, pp. 292–298. doi: 10.1109/ICCCIT62592.2025.10928115.
- A. Hussain, A. Khatoon, A. Aslam, and M. A. Khosa, “A Comparative Performance Analysis of Machine Learning Models for Intrusion Detection Classification,” Journal of Cyber Security, vol. 6, no. 1, pp. 1–23, 2024, doi: 10.32604/jcs.2023.046915.
- X. Zhao, “Real-time Application of Intrusion Detection Algorithm Based on Machine Learning in Security System,” in 2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC), IEEE, Aug. 2024, pp. 752–757. doi: 10.1109/PEEEC63877.2024.00141.
- S. Qadir Mohammed and M. A. Hussein, “Performance Analysis of different Machine Learning Models for Intrusion Detection Systems,” Journal of Engineering, vol. 28, no. 5, pp. 61–91, May 2022, doi: 10.31026/j.eng.2022.05.05.
- M. Udurume, V. Shakhov, and I. Koo, “Comparative Evaluation of Network-Based Intrusion Detection: Deep Learning vs Traditional Machine Learning Approach,” in 2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, Jul. 2024, pp. 520–525. doi: 10.1109/ICUFN61752.2024.10625037.
- S. Sreelakshmi, A. A. Babu, C. Lakshmipriya, L. A. Anto Gracious, M. Nalini, and R. Siva Subramanian, “Enhancing Intrusion Detection Systems with Machine Learning,” in 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), IEEE, Oct. 2024, pp. 557–564. doi: 10.1109/ICSSAS64001.2024.10760341.
- Y. Wang, “Deep Learning-Based Network Intrusion Detection Systems,” Applied and Computational Engineering, vol. 109, no. 1, pp. 179–188, Dec. 2024, doi: 10.54254/2755-2721/2024.18104.
- J. Simioni, E. K. Viegas, A. Santin, and P. Horchulhack, “An Early Exit Deep Neural Network for Fast Inference Intrusion Detection,” in Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, New York, NY, USA: ACM, Mar. 2025, pp. 730–737. doi: 10.1145/3672608.3707974.
- R. Picot, F. Gohring de Magalhães, A. Shahnejat Bushehri, M. Ben Atti, G. Nicolescu, and A. Quintero, “Protocol-Agnostic and Packet-Based Intrusion Detection Using a Multi-Layer Deep-Learning Architecture at the Network Edge,” IEEE Access, vol. 13, pp. 57867–57877, 2025, doi: 10.1109/ACCESS.2025.3555201.
- H. Zhang, L. Ge, G. Zhang, J. Fan, D. Li, and C. Xu, “A two-stage intrusion detection method based on light gradient boosting machine and autoencoder,” Mathematical Biosciences and Engineering, vol. 20, no. 4, pp. 6966–6992, 2023, doi: 10.3934/mbe.2023301.
- M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection,” Multimed Tools Appl, vol. 82, no. 15, pp. 23615–23633, Jun. 2023, doi: 10.1007/s11042-023-14795-2.
- D. Kshirsagar and S. Kumar, “Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques,” Cyber-Physical Systems, vol. 9, no. 3, pp. 244–259, Jul. 2023, doi: 10.1080/23335777.2021.2023651.
- Q. Liu and Y. Li, “Research on Intrusion Detection Model Based on Filter Feature Selection Algorithm,” in 2024 8th International Conference on Communication and Information Systems (ICCIS), IEEE, Oct. 2024, pp. 120–125. doi: 10.1109/ICCIS63642.2024.10779410.
- M. A. Umar, Z. Chen, K. Shuaib, and Y. Liu, “Effects of feature selection and normalization on network intrusion detection,” Data Science and Management, vol. 8, no. 1, pp. 23–39, Mar. 2025, doi: 10.1016/j.dsm.2024.08.001.
- H. Zouhri, A. Idri, and A. Ratnani, “Evaluating the impact of filter-based feature selection in intrusion detection systems,” Int J Inf Secur, vol. 23, no. 2, pp. 759–785, Apr. 2024, doi: 10.1007/s10207-023-00767-y.
- G. N. N. Barbosa, M. Andreoni, and D. M. F. Mattos, “Optimizing feature selection in intrusion detection systems: Pareto dominance set approaches with mutual information and linear correlation,” Ad Hoc Networks, vol. 159, p. 103485, Jun. 2024, doi: 10.1016/j.adhoc.2024.103485.
- Y. Zhang, H. Zhang, and B. Zhang, “An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection,” Information, vol. 13, no. 7, p. 314, Jun. 2022, doi: 10.3390/info13070314.
- N. Moustafa and J. Slay, “UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in 2015 Military Communications and Information Systems Conference (MilCIS), IEEE, Nov. 2015, pp. 1–6. doi: 10.1109/MilCIS.2015.7348942.
- S. B. Mallampati and H. Seetha, “An Integrated Feature Extraction Based on Principal Components and Deep Auto Encoder with Extra Tree for Intrusion Detection Systems,” Journal of Information & Knowledge Management, vol. 23, no. 01, Feb. 2024, doi: 10.1142/S0219649223500661.
- Md. B. Pranto, Md. H. A. Ratul, Md. M. Rahman, I. J. Diya, and Z.-B. Zahir, “Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy - A Network Intrusion Detection System,” Journal of Advances in Information Technology, vol. 13, no. 1, 2022, doi: 10.12720/jait.13.1.36-44.
- H. A. Al Essa and W. S. Bhaya, “Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 665–676, Feb. 2024, doi: 10.11591/eei.v13i1.5844.
- S. Shakeela, N. S. Shankar, P. M. Reddy, T. K. Tulasi, and M. M. Koneru, “Optimal ensemble learning based on distinctive feature selection by univariate ANOVA-F statistics for IDS,” International Journal of Electronics and Telecommunications, vol. 67, no. 2, pp. 267–275, 2021, doi: 10.24425/ijet.2021.135975.
References
N. Challa, “Unveiling the Shadows: A Comprehensive Exploration of Advanced Persistent Threats (APTs) and Silent Intrusions in Cybersecurity,” Journal of Artificial Intelligence & Cloud Computing, pp. 1–5, Dec. 2022, doi: 10.47363/JAICC/2022(1)190.
I. A. Kandhro et al., “Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures,” IEEE Access, vol. 11, pp. 9136–9148, 2023, doi: 10.1109/ACCESS.2023.3238664.
B. Alotaibi, “A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities,” Sensors, vol. 23, no. 17, p. 7470, Aug. 2023, doi: 10.3390/s23177470.
N. Singh, R. Buyya, and H. Kim, “Securing Cloud-Based Internet of Things: Challenges and Mitigations,” Sensors, vol. 25, no. 1, p. 79, Dec. 2024, doi: 10.3390/s25010079.
H. A. Hassan, E. E. Hemdan, W. El-Shafai, M. Shokair, and F. E. A. El-Samie, “Intrusion Detection Systems for the Internet of Thing: A Survey Study,” Wirel Pers Commun, vol. 128, no. 4, pp. 2753–2778, Feb. 2023, doi: 10.1007/s11277-022-10069-6.
Z. Dai et al., “An intrusion detection model to detect zero-day attacks in unseen data using machine learning,” PLoS One, vol. 19, no. 9, Sep. 2024, doi: 10.1371/journal.pone.0308469.
K. Bonagiri, P. Krishnamoorthy, V. Keerthiga, D. Kirubakaran, R. David, and B. Nancharaiah, “Cybersecurity With Machine Learning: Implementing AI Algorithms for Intrusion Prevention, Advanced Data Protection, and Real-Time Threat Analysis,” in 2025 International Conference on Computational, Communication and Information Technology (ICCCIT), IEEE, Feb. 2025, pp. 292–298. doi: 10.1109/ICCCIT62592.2025.10928115.
A. Hussain, A. Khatoon, A. Aslam, and M. A. Khosa, “A Comparative Performance Analysis of Machine Learning Models for Intrusion Detection Classification,” Journal of Cyber Security, vol. 6, no. 1, pp. 1–23, 2024, doi: 10.32604/jcs.2023.046915.
X. Zhao, “Real-time Application of Intrusion Detection Algorithm Based on Machine Learning in Security System,” in 2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC), IEEE, Aug. 2024, pp. 752–757. doi: 10.1109/PEEEC63877.2024.00141.
S. Qadir Mohammed and M. A. Hussein, “Performance Analysis of different Machine Learning Models for Intrusion Detection Systems,” Journal of Engineering, vol. 28, no. 5, pp. 61–91, May 2022, doi: 10.31026/j.eng.2022.05.05.
M. Udurume, V. Shakhov, and I. Koo, “Comparative Evaluation of Network-Based Intrusion Detection: Deep Learning vs Traditional Machine Learning Approach,” in 2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, Jul. 2024, pp. 520–525. doi: 10.1109/ICUFN61752.2024.10625037.
S. Sreelakshmi, A. A. Babu, C. Lakshmipriya, L. A. Anto Gracious, M. Nalini, and R. Siva Subramanian, “Enhancing Intrusion Detection Systems with Machine Learning,” in 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), IEEE, Oct. 2024, pp. 557–564. doi: 10.1109/ICSSAS64001.2024.10760341.
Y. Wang, “Deep Learning-Based Network Intrusion Detection Systems,” Applied and Computational Engineering, vol. 109, no. 1, pp. 179–188, Dec. 2024, doi: 10.54254/2755-2721/2024.18104.
J. Simioni, E. K. Viegas, A. Santin, and P. Horchulhack, “An Early Exit Deep Neural Network for Fast Inference Intrusion Detection,” in Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, New York, NY, USA: ACM, Mar. 2025, pp. 730–737. doi: 10.1145/3672608.3707974.
R. Picot, F. Gohring de Magalhães, A. Shahnejat Bushehri, M. Ben Atti, G. Nicolescu, and A. Quintero, “Protocol-Agnostic and Packet-Based Intrusion Detection Using a Multi-Layer Deep-Learning Architecture at the Network Edge,” IEEE Access, vol. 13, pp. 57867–57877, 2025, doi: 10.1109/ACCESS.2025.3555201.
H. Zhang, L. Ge, G. Zhang, J. Fan, D. Li, and C. Xu, “A two-stage intrusion detection method based on light gradient boosting machine and autoencoder,” Mathematical Biosciences and Engineering, vol. 20, no. 4, pp. 6966–6992, 2023, doi: 10.3934/mbe.2023301.
M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection,” Multimed Tools Appl, vol. 82, no. 15, pp. 23615–23633, Jun. 2023, doi: 10.1007/s11042-023-14795-2.
D. Kshirsagar and S. Kumar, “Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques,” Cyber-Physical Systems, vol. 9, no. 3, pp. 244–259, Jul. 2023, doi: 10.1080/23335777.2021.2023651.
Q. Liu and Y. Li, “Research on Intrusion Detection Model Based on Filter Feature Selection Algorithm,” in 2024 8th International Conference on Communication and Information Systems (ICCIS), IEEE, Oct. 2024, pp. 120–125. doi: 10.1109/ICCIS63642.2024.10779410.
M. A. Umar, Z. Chen, K. Shuaib, and Y. Liu, “Effects of feature selection and normalization on network intrusion detection,” Data Science and Management, vol. 8, no. 1, pp. 23–39, Mar. 2025, doi: 10.1016/j.dsm.2024.08.001.
H. Zouhri, A. Idri, and A. Ratnani, “Evaluating the impact of filter-based feature selection in intrusion detection systems,” Int J Inf Secur, vol. 23, no. 2, pp. 759–785, Apr. 2024, doi: 10.1007/s10207-023-00767-y.
G. N. N. Barbosa, M. Andreoni, and D. M. F. Mattos, “Optimizing feature selection in intrusion detection systems: Pareto dominance set approaches with mutual information and linear correlation,” Ad Hoc Networks, vol. 159, p. 103485, Jun. 2024, doi: 10.1016/j.adhoc.2024.103485.
Y. Zhang, H. Zhang, and B. Zhang, “An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection,” Information, vol. 13, no. 7, p. 314, Jun. 2022, doi: 10.3390/info13070314.
N. Moustafa and J. Slay, “UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in 2015 Military Communications and Information Systems Conference (MilCIS), IEEE, Nov. 2015, pp. 1–6. doi: 10.1109/MilCIS.2015.7348942.
S. B. Mallampati and H. Seetha, “An Integrated Feature Extraction Based on Principal Components and Deep Auto Encoder with Extra Tree for Intrusion Detection Systems,” Journal of Information & Knowledge Management, vol. 23, no. 01, Feb. 2024, doi: 10.1142/S0219649223500661.
Md. B. Pranto, Md. H. A. Ratul, Md. M. Rahman, I. J. Diya, and Z.-B. Zahir, “Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy - A Network Intrusion Detection System,” Journal of Advances in Information Technology, vol. 13, no. 1, 2022, doi: 10.12720/jait.13.1.36-44.
H. A. Al Essa and W. S. Bhaya, “Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 665–676, Feb. 2024, doi: 10.11591/eei.v13i1.5844.
S. Shakeela, N. S. Shankar, P. M. Reddy, T. K. Tulasi, and M. M. Koneru, “Optimal ensemble learning based on distinctive feature selection by univariate ANOVA-F statistics for IDS,” International Journal of Electronics and Telecommunications, vol. 67, no. 2, pp. 267–275, 2021, doi: 10.24425/ijet.2021.135975.