Autors: Rusev, A. N., Tsochev, G. R., Trifonov, R. I.
Title: Development of a centralized intrusion detection system using machine learning
Keywords: Machine learning algorithms , intrusion detection systems, D

Abstract: In the world of Internet many people become victim of cyberattacks. One of the most devastating threats are DoS and DDoS attacks. In this paper is suggested an Intrusion Detection System in which are implemented machine learning models. SVM classification algorithm is used for making three different machine learning models. Each machine model has specifically chosen features which characterize a different type of DoS attack. All machine learning models are integrated in the proposed IDS.

References

    Issue

    The Tenth International Workshop on Mathematical Models and their Applications, vol. 2700, issue 3001, 2023, Russia, AIP Conference Proceedings, https://doi.org/10.1063/5.0124920

    Цитирания (Citation/s):
    1. A. Mittal, A. Gupta, Bhoomi and K. Agarwal, "Anomaly Detection in Cybersecurity: Leveraging Machine Learning for Intrusion Detection," 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), Gautam Buddha Nagar, India, 2024, pp. 1-5, doi: 10.1109/IC3SE62002.2024.10592923. - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus