Autors: Tsochev, G. R., Trifonov, R. I., Radoslav Yoshinov., Slavcho Manolov., Pavlova, G. V.
Title: Improving the Efficiency of IDPS by using Hybrid Methods from Artificial Intelligence
Keywords: multi-agent systems , artificial intelligence , network and

Abstract: The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.

References

    Issue

    International Conference on Information Technologies (InfoTech), 2019, Bulgaria, ISBN 978-1-7281-3274-7

    Цитирания (Citation/s):
    1. A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion Detection - 2020 - в издания, индексирани в Scopus или Web of Science
    2. Analysis of Artificial Intelligence (AI) Enhanced Technologies in Support of Cyber Defense: Advantages, Challenges, and Considerations for Future Deployment - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    3. Ivan Garvanov, Vladimir Jotsov, Magdalena Garvanova, "Data Science Modeling for EEG Signal Filtering Using Wavelet Transforms", Intelligent Systems (IS) 2020 IEEE 10th International Conference on, pp. 352-357, 2020 - 2020 - в издания, индексирани в Scopus или Web of Science
    4. Imtithal A. Saeed, Ali Selamat, Mohd Foad Rohani, Ondrej Krejcar, Junaid Ahsenali Chaudhry, "A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion Detection", Access IEEE, vol. 8, pp. 180184-180209, 2020 - 2020 - в издания, индексирани в Scopus или Web of Science
    5. Feltus, Christophe. "Current and Future RL’s Contribution to Emerging Network Security." Procedia Computer Science 177 (2020): 516-521. - 2020 - в издания, индексирани в Scopus или Web of Science
    6. Feltus, C. (2021). AI’S Contribution to Ubiquitous Systems and Pervasive Networks Security–Reinforcement Learning vs Recurrent Networks. Journal of Ubiquitous Systems & Pervasive Networks, 15(2), 01-09. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    7. Feltus, C. (2020). Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems: Systematization of Knowledge. International Journal of Distributed Artificial Intelligence (IJDAI), 12(2), 35-55. - 2020 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus