The rapid growth of Information Technology and the COVID-19 pandemic have significantly increased internet usage worldwide, leading to a surge in cyber threats. Network Intrusion Detection Systems (NIDS) are essential for monitoring and mitigating unauthorized intrusions, but class imbalance among attack types often hampers performance, particularly for minority samples. This research proposes an Optimized Random Under sampling Boosting Model using Particle Swarm Optimization algorithm to enhance the detection and classification of five intrusion types: DOS, Probe, R2L, U2R, and Normal. The model outperformed state-of-the-art models such as JN8, KNN, and GA–LSTM–RNA, achieving near-perfect performance with approximately 100% accuracy for all attack types. In terms of precision, the model achieved 99.990% for DOS, 99.886% for Probe, 97.689% for R2L, 73.684% for U2R, and 99.990% for Normal. The recall rates were similarly impressive, with 99.993% for DOS, 99.943% for Probe, 99.329% for R2L, 93.333% for U2R, and 99.886% for Normal. The F1-scores were 99.956% for DOS, 99.914% for Probe, 98.502% for R2L, 82.353% for U2R, and 99.938% for Normal. In general, the proposed ORUSBEM model was able to effectively detect each type of attack, including the normal type. The results revealed the effectiveness of the developed technique in enhancing the accuracy and efficiency of the intrusion detection system. The result is beneficiary in improving the security of various applications by detecting and preventing malicious attacks. The model executed 100 iterations in 185.1 seconds, indicating significant optimization improvements. While BPSO corroborate to be effective in selecting the optimal feature subset, other optimization techniques such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), or Differential Evolution (DE) can be used to compare and optimize the results in the future. However, focusing on enhancing U2R detection and reducing execution time is also an area that needs serious attention. Additionally, testing the model in real-life applications is crucial for strengthening cyber security in industries and organizations.

Abubakar, M., Surajo, Y. and Tasiu , S. (2025). An Explainable Deep Learning Model for Illegal Dress Code Detection and Classification. Journal of Basics and Applied Sciences Research, 3(1), 1-10. https://dx.doi.org/10.4314/jobasr.v3i1.1 Amru, M., Kannan, R. J., Ganesh, E. N., Muthumarilakshmi, S., Padmanaban, K., Jeyapriya, J., &Murugan, S. (2024). Network intrusion detection system by applying ensemble model for smart home.International Journal of Electrical and Computer Engineering, 14(3), 3485–3494. https://doi.org/10.11591/ijece.v14i3, pp3485-3494 Cao, B., Li, C., Song, Y., Qin, Y., Sciences, C. C.-A., & 2022, U. (2022). Network Intrusion Detection Model Based on CNN and GRU. Mdpi.ComSign In. https://doi.org/10.3390/app12094184 Imrana S., Obunadike G.N, Abubakar, M. (2025). Machine Learning-Based Framework for Predicting User Satisfaction in E-Learning Systems. Journal of Basics and Applied Sciences Research, 3(2), 78-85. https://dx.doi.org/10.4314/jobasr.v3i2.9 Kumar, V., Das, A. K., & Sinha, D. (2021). UIDS: a unified intrusion detection system for IoT environment. Evolutionary Intelligence, 14(1), 47–59. https://doi.org/10.1007/s12065-019-00291-w Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F. C., Larijani, H., Raza, A., & Hussain, A. (2018). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10989 LNAI(August), 759–769. https://doi.org/10.1007/978-3-030-00563-4_74 Muhuri, P. S., Chatterjee, P., Yuan, X., Roy, K., & Esterline, A. (2020a). Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks. Information (Switzerland), 11(5). https://doi.org/10.3390/INFO11050243 Mulyanto, M., Faisal, M., Prakosa, S. W., & Leu, J. S. (2021). Effectiveness of focal loss for minority classification in network intrusion detection systems. Symmetry, 13(1), 1–16. https://doi.org/10.3390/sym13010004 Nagaraja, A., Boregowda, U., Khatatneh, K., Vangipuram, R., Nuvvusetty, R., & Sravan Kiran, V. (2020a). Similarity Based Feature Transformation for Network Anomaly Detection. IEEE Access, 8, 39184–39196. https://doi.org/10.1109/ACCESS.2020.2975716 Qazi, E. U. H., Faheem, M. H., & Zia, T. (2023).HDLNIDS: Hybrid deep-learning-based network intrusion detection system. Applied Sciences, 13(8), 4921.https://doi.org/10.3390/app13084921 Sarumi, O. A., Adetunmbi, A. O., & Adetoye, F. A. (2020a). Discovering computer networks intrusion using data analytics and machine intelligence. Scientific African, 9. https://doi.org/10.1016/j.sciaf.2020.e00500 Sivamohan, S., & Sridhar, S. S. (2023). An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework. Neural Computing and Applications, 35(15), 11459–11475. https://doi.org/10.1007/s00521-023-08319-0 Tang, C., Luktarhan, N., & Zhao, Y. (2020). An efficient intrusion detection method based on LightGBM and autoencoder. Symmetry, 12(9). https://doi.org/10.3390/sym12091458 Thapa, N., Liu, Z., Kc, D. B., Gokaraju, B., & Roy, K. (2020a). Comparison of machine learning and deep learning models for network intrusion detection systems. Future Internet, 12(10), 1–16. https://doi.org/10.3390/fi12100167 Wu, Z., Wang, J., Hu, L., Zhang, Z., & Wu, H. (2020). A network intrusion detection method based on semantic Re-encoding and deep learning. Journal of Network and Computer Applications, 164. https://doi.org/10.1016/j.jnca.2020.102688 Zhou, Y., Cheng, G., Jiang, S., & Dai, M. (2020a). Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer Networks,174. https://doi.org/10.1016/j.comnet.2020.107247