AI-Enabled Threat Detection in Network Security

  • Authors

    • Seppo Linnainmaa Professor of Computer Science, University of Helsinki, Finland Author
    • Arto Salomaa Professor of Computer Science, University of Turku, Finland Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PII6K4X

    Published 07-03-2024

  • Artificial Intelligence, Network Security, Threat Detection, Machine Learning, Deep Learning, Intrusion Detection System, Cybersecurity

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    S. Linnainmaa and A. Salomaa, “AI-Enabled Threat Detection in Network Security”, ijmiet, vol. 7, no. 2, pp. 01–15, Jul. 2024, doi: 10.67228/30715628/IJMIET-2024PII6K4X.

  • Abstract

    Modern networks face increasing cyber threats such as malware, ransomware, phishing, DDoS, insider attacks, and advanced persistent threats, making traditional signature-based security systems less effective. Artificial Intelligence (AI), through machine learning and deep learning, enables intelligent threat detection by identifying known and unknown attacks in real time. This study proposes an AI-based threat detection framework that integrates network traffic analysis, preprocessing, feature engineering, threat classification, and automated response. Experimental evaluation using metrics such as accuracy, precision, recall, F1-score, false positive rate, and detection latency demonstrates that the proposed framework outperforms conventional methods by providing higher detection accuracy, lower false alarms, and faster response. Despite challenges related to data quality, model interpretability, and computational cost, AI-driven cybersecurity offers a scalable and effective solution for modern network security.

  • References

    [1] Snort, “Snort – Network Intrusion Detection and Prevention System,” Cisco Systems, 2024.

    [2] Dorothy E. Denning, “An Intrusion-Detection Model,” IEEE Transactions on Software Engineering, vol. 13, no. 2, pp. 222–232, 1987.

    [3] Wenke Lee and Salvatore J. Stolfo, “Data Mining Approaches for Intrusion Detection,” USENIX Security Symposium, pp. 79–93, 1998.

    [4] M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Dataset,” IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6, 2009.

    [5] N. Moustafa and J. Slay, “UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems,” Military Communications and Information Systems Conference, pp. 1–6, 2015.

    [6] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.

    [7] T. Kim, “Long Short-Term Memory Recurrent Neural Network Classifier for Intrusion Detection,” International Conference on Platform Technology and Service, pp. 1–5, 2016.

    [8] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [9] A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A Deep Learning Approach for Network Intrusion Detection System,” EAI International Conference on Bio-Inspired Information and Communications Technologies, pp. 21–26, 2016.

    [10] Q. Niyaz, W. Sun, and A. Javaid, “A Deep Learning Based DDoS Detection System in Software-Defined Networking,” IEEE Access, vol. 4, pp. 1–10, 2016.

    [11] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

    [12] C. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, vol. 20, pp. 273–297, 1995.

    [13] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016.

    [14] N. Papernot, P. McDaniel, and I. Goodfellow, “Practical Black-Box Attacks against Machine Learning,” ACM Asia Conference on Computer and Communications Security, pp. 506–519, 2017.

    [15] National Institute of Standards and Technology, “Guide to Intrusion Detection and Prevention Systems (IDPS),” NIST Special Publication 800-94, 2022.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

  • Downloads

Similar Articles

11-20 of 72

You may also start an advanced similarity search for this article.