Abstract
With the increasingly complex network environment, intrusion detection systems are faced with severe challenges such as high-dimensional feature redundancy, category imbalance and low detection accuracy. Aiming at these problems, this paper proposes a multi-objective and multi-strategy collaborative optimization intrusion detection model (MCO-IDM). The model innovatively integrates multi-objective optimization techniques to simultaneously optimize conflicting objectives such as minimizing the number of features, maximizing detection accuracy and minimizing false alarm rates, and integrates collaborative search strategies such as dynamic adaptive mechanisms and swarm intelligence optimization algorithms (such as CMO-BOA) to achieve efficient trade-offs through Pareto frontier search and weight adjustment. The test results show that on globally public datasets such as KDD CUP99, NSL-KDD and CIC-IDS2017, MCO-IDM achieves the highest accuracy rate of 97.8%, the false alarm rate is reduced to 4.3%, and the training time is controlled within 185.3 seconds. At the same time, it maintains high robustness and scalability under different data scales. These results confirm the effectiveness of the model in feature selection, parameter optimization and multi-policy collaboration, and provide a new scheme with high precision and strong practicability for network intrusion detection.
References
Ullah, F., Ullah, S., Srivastava, G., and Lin, J. C. W. (2024). IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic. Digital Communications and Networks, 10(1), 190–204.
Gavrylenko, S., Poltoratskyi, V., and Nechyporenko, A. (2024). Intrusion detection model based on improved transformer. Advanced Information Systems, 8(1), 94–99.
Chen, X., Gong, Z., Huang, D., Jiang, N., and Zhang, Y. (2024, August). Overcoming Class Imbalance in Network Intrusion Detection: A Gaussian Mixture Model and ADASYN Augmented Deep Learning Framework. In Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning (pp. 48–53).
Rahim, K., Nasir, Z. U. I., Ikram, N., and Qureshi, H. K. (2025). Integrating contextual intelligence with mixture of experts for signature and anomaly-based intrusion detection in CPS security. Neural Computing and Applications, 37(8), 5991–6007.
Chatterjee, S., Shaw, V., and Das, R. (2024). Multi-stage intrusion detection system aided by grey wolf optimization algorithm. Cluster Computing, 27(3), 3819–3836.
Ashiku, L., and Dagli, C. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185(1), 239–247.
Wu, Z., Zhang, H., Wang, P., and Sun, Z. (2022). RTIDS: A robust transformer-based approach for intrusion detection system. IEEE Access, 10(1), 64375–64387.
Mighan, S. N., and Kahani, M. (2021). A novel scalable intrusion detection system based on deep learning. International Journal of Information Security, 20(3), 387–403.
Du, J., Yang, K., Hu, Y., and Jiang, L. (2023). NIDS-CNNLSTM: Network intrusion detection classification model based on deep learning. IEEE Access, 11(1), 24808–24821.
Awajan, A. (2023). A novel deep learning-based intrusion detection system for IoT networks. Computers, 12(2), 34–45.
Logeswari, G., Bose, S., and Anitha, T. J. I. A. (2023). An intrusion detection system for SDN using machine learning. Intelligent Automation & Soft Computing, 35(1), 867–880.
Attou, H., Guezzaz, A., Benkirane, S., Azrour, M., and Farhaoui, Y. (2023). Cloud-based intrusion detection approach using machine learning techniques. Big Data Mining and Analytics, 6(3), 311–320.
Bhavsar, M., Roy, K., Kelly, J., and Olusola, O. (2023). Anomaly-based intrusion detection system for IoT application. Discover Internet of things, 3(1), 5–16.
Kumar, V., Das, A. K., and Sinha, D. (2021). UIDS: a unified intrusion detection system for IoT environment. Evolutionary intelligence, 14(1), 47–59.
Al-Daweri, M. S., Zainol Ariffin, K. A., Abdullah, S., and Md. Senan, M. F. E. (2020). An analysis of the KDD99 and UNSW-NB15 datasets for the intrusion detection system. Symmetry, 12(10), 1666–1677.
Abrar, I., Ayub, Z., Masoodi, F., and Bamhdi, A. M. (2020, September). A machine learning approach for intrusion detection system on NSL-KDD dataset. In 2020 international conference on smart electronics and communication (ICOSEC) (pp. 919–924). IEEE.
Sarhan, M., Layeghy, S., and Portmann, M. (2022). Towards a standard feature set for network intrusion detection system datasets. Mobile networks and applications, 27(1), 357–370.
Jiang, K., Wang, W., Wang, A., and Wu, H. (2020). Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE access, 8(1), 32464–32476.
Qazi, E. U. H., Faheem, M. H., and Zia, T. (2023). HDLNIDS: hybrid deep-learning-based network intrusion detection system. Applied Sciences, 13(8), 4921–4932.
Talukder, M. A., Hasan, K. F., Islam, M. M., Uddin, M. A., Akhter, A., Yousuf, M. A., …and Moni, M. A. (2023). A dependable hybrid machine learning model for network intrusion detection. Journal of Information Security and Applications, 72(1), 103405–103416.
Sajid, M., Malik, K. R., Almogren, A., Malik, T. S., Khan, A. H., Tanveer, J., and Rehman, A. U. (2024). Enhancing intrusion detection: a hybrid machine and deep learning approach. Journal of Cloud Computing, 13(1), 123–135.
Injadat, M., Moubayed, A., Nassif, A. B., and Shami, A. (2020). Multi-stage optimized machine learning framework for network intrusion detection. IEEE Transactions on Network and Service Management, 18(2), 1803–1816.
Iyer, K. I. (2021). From Signatures to Behavior: Evolving Strategies for Next-Generation Intrusion Detection. European Journal of Advances in Engineering and Technology, 8(6), 165–171.
Bhati, B. S., and Rai, C. S. (2020). Analysis of support vector machine-based intrusion detection techniques. Arabian Journal for Science and Engineering, 45(4), 2371–2383.
Azizan, A. H., Mostafa, S. A., Mustapha, A., Foozy, C. F. M., Wahab, M. H. A., Mohammed, M. A., and Khalaf, B. A. (2021). A machine learning approach for improving the performance of network intrusion detection systems. Annals of Emerging Technologies in Computing (AETiC), 5(5), 201–208.
Verkerken, M., D’hooge, L., Sudyana, D., Lin, Y. D., Wauters, T., Volckaert, B., and De Turck, F. (2023). A novel multi-stage approach for hierarchical intrusion detection. IEEE Transactions on Network and Service Management, 20(3), 3915–3929.
Nguyen, M. T., and Kim, K. (2020). Genetic convolutional neural network for intrusion detection systems. Future Generation Computer Systems, 113(1), 418–427.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Journal of Cyber Security and Mobility