Particle Swarm Optimization-Based Artificial Neural Network for Network Intrusion Detection

References

[1] Y. Li and Q. Liu, “A comprehensive review study of cyber-attacks and cyber security: Emerging trends and recent developments,” Energy Reports, vol. 7, pp. 1220–1232, 2021, [Online]. Available: https://doi.org/10.1016/j.egyr.2021.08.126

[2] P. Dini, A. Elhanashi, A. Begni, S. Saponara, Q. Zheng, and K. Gasmi, “Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity,” Applied Sciences, vol. 13, no. 13, p. 7507, 2023, [Online]. Available: https://doi.org/10.3390/app13137507

[3] M. T. Abdelaziz, A. Radwan, H. Mamdouh, A. S. Saad, A. S. Abuzaid, A. A. AbdElhakeem, S. Zakzouk, K. Moussa, and M. S. Darweesh, “Enhancing network threat detection with random forest-based NIDS and permutation feature importance,” Journal of Network and Systems Management, vol. 33, no. 2, Art. no. 2, 2025, [Online]. Available: https://doi.org/10.1007/s10922-024-09856-9

[4] Y. Feng and K. Sakurai, “Network intrusion detection: Evolution from conventional approaches to LLM collaboration and emerging risks,” arXiv preprint arXiv:2510.23313, 2025, doi: 10.48550/arXiv.2510.23313. License: CC BY 4.0

[5] B. R. Kikissagbe and M. Adda, “Machine learning-based intrusion detection methods in IoT systems: A comprehensive review,” Electronics, vol. 13, no. 18, p. 3601, 2024, [Online]. Available: https://doi.org/10.3390/electronics13183601

[6] M. Cantone, C. Marrocco, and A. Bria, “Machine learning in network intrusion detection: A cross-dataset generalization study,” IEEE Access, vol. 12, pp. 144489–144508, 2024, doi: 10.1109/ACCESS.2024.3472907.

[7] Machine learning based intrusion detection framework for detecting security attacks in Internet of Things, Scientific Reports, vol. 14, Art. no. 30275, Dec. 2024. [Online]. Available: https://doi.org/10.1038/s41598-024-30275

[8] Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability, Frontiers in Computer Science, vol. 7, Sec. Computer Security, May 2025. [Online]. Available: https://doi.org/10.3389/fcomp.2025.1520741

[9] Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study, Applied Sciences, vol. 15, no. 4, p. 1903, Feb. 2025. [Online]. Available: https://doi.org/10.3390/app15041903

[10] Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction, arXiv preprint, arXiv:2401.12262 [cs.CR], Cornell University, Jan. 2024. [Online]. Available: https://arxiv.org/abs/2401.12262

[11] Artificial Neural Network Model for Intrusion Detection System, Mediterranean Journal of Basic and Applied Sciences (MJBAS), vol. 6, no. 1, pp. 20–26, Jan.–Mar. 2022. [Online]. Available: http://doi.org/10.46382/MJBAS.2022.6103

[12] A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems, Array, vol. 26, p. 100406, July 2025. [Online]. Available: https://doi.org/10.1016/j.array.2025.100406

[13] Dhoogla. (2018). EMBER 2018 v2 features [Dataset]. Kaggle. https://www.kaggle.com/datasets/dhoogla/ember-2018-v2-features

[14] Canadian Institute for Cybersecurity. (2017). CIC-IDS2017: Intrusion detection evaluation dataset [Dataset]. University of New Brunswick. https://www.unb.ca/cic/datasets/ids-2017.html

@article{1711751,
      author = {Michael Manasseh Dalaky, Gideon yunius Giroh},
      title = {Particle Swarm Optimization-Based Artificial Neural Network for Network Intrusion Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {242-247},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711751.pdf},
      abstract = {The growing complexity of cyberattacks has made efficient intrusion and malware detection an urgent challenge. This study addresses this problem by integrating Particle Swarm Optimization (PSO) with Artificial Neural Networks (ANN) to enhance classification accuracy while reducing computational costs. Using the CIC-IDS2017 and EMBER 2018 benchmark datasets, PSO was employed to select the most relevant features that improve the ANN?s predictive capability. The approach was compared with models trained on all available features to evaluate the trade-off between dimensionality reduction and performance. Results showed that the PSO-selected feature model achieved higher accuracy and efficiency, with the CIC-IDS2017 dataset recording precision and accuracy values of 99.4% and 99.78%, the EMBER 2018 dataset reaching 96% accuracy. These findings demonstrate that PSO effectively eliminates redundant features, leading to faster convergence and improved generalization. The proposed PSO-ANN framework offers a scalable and robust solution for intrusion and malware detection, contributing to the advancement of intelligent cybersecurity systems.},
      keywords = {Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Feature Selection, Intrusion Detection},
      month = {November},
  }