Autors: Bosale, K. S., Nenova, M. V., Iliev, G. L.
Title: Data Mining Based Advanced Algorithm for Intrusion Detections in Communication Networks
Keywords: Intrusion detection , Data Mining , Classifier , Feature Selection Algorithm , communication networks

Abstract: Nowadays the network security is the important topic to research. The Network Security from different types of attacks which is R2L, U2R, and DoS. It is very challenging tasks due to variety of research problems like noise, large data size, inefficient features selection method etc. Network Intrusion Detection System (IDS), as the basic security protection technique, is generally used limiting such malicious attacks. In this project, we are presenting the efficient IDS solution using filter based feature choice strategy. We are exhibiting the hybrid feature determination method. The Intrusion Detection System (IDS) examine the main part in distortion and ambushes in the framework. In this examination work, data mining methods unite with association rule features extraction and classifier. In this paper, we proposed filter based hybrid feature selection algorithm (HFSA), most relevant features are retained and used to construct classifiers for respective classes..



    International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 2018, 2018, India, IEEE, DOI 10.1109/CTEMS.2018.8769173

    Цитирания (Citation/s):
    1. Lalit Mohan, Sourabh Jain, Priyanka Suyal, Arvind Kumar, "Data mining Classification Techniques for Intrusion Detection System", Computational Intelligence and Communication Networks (CICN) 2020 12th International Conference on, pp. 351-355, 2020 - 2020 - в издания, индексирани в Scopus или Web of Science
    2. S. Raj, K. N. Singh, N. K. Gupta, R. Nigam, B. Verma and S. Karsoliya, "High Accuracy of Hybrid IDS System using Evidence Theory and SVM ML Technique," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 1261-1264, doi: 10.1109/ICAIS50930.2021.9396054. - 2021 - в издания, индексирани в Scopus или Web of Science
    3. SALIH, Azar Abid; ABDULAZEEZ, Adnan Mohsin. Evaluation of classification algorithms for intrusion detection system: A review. Journal of Soft Computing and Data Mining, 2021, 2.1: 31-40. Не; e-ISSN : 2716-621X - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    4. Islam, S., Rouf, M. A., Shahariar Parvez, A. H. M., & Podder, P. (2022). Machine Learning-Driven Algorithms for Network Anomaly Detection. In Inventive Computation and Information Technologies (pp. 493-507). Springer, Singapore. - 2022 - в издания, индексирани в Scopus или Web of Science
    5. Mendoza, A. M., Hernandez, R. M., Amorado, R. V., Coliat, M. A., & De Chavez, P. I. C. (2022, August). Network Data Feature Selection in Detecting Network Intrusion using Supervised Machine Learning Techniques. In 2022 2nd Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-6). IEEE. - 2022 - в издания, индексирани в Scopus или Web of Science
    6. Manimegalai, T., Ravishankar, T. N., Kannagi, L., Kannan, K., & Anitha, G. (2022, April). A Novel approach for Data mining Classification using J48DT Classifier for Intrusion Detection System. In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 601-607). IEEE. - 2022 - в издания, индексирани в Scopus или Web of Science
    7. Singh, A., Bhandari, V., & Srivastava, R. (2022, May). Optimization Accuracy of Intrusion Detection of Imbalanced Network using PCA and Conv1D-LSTM Technique. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science