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 Sel

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..

References

    Issue

    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
    8. Alasad, Q., Hammood, M.M., Alahmed, S. (2023). Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset. In: Al-Sharafi, M.A., Al-Emran, M., Al-Kabi, M.N., Shaalan, K. (eds) Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems. ICETIS 2022. Lecture Notes in Networks and Systems, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-031-25274-7_45 - 2023 - в издания, индексирани в Scopus или Web of Science
    9. Abbas Q, Hina S, Sajjad H, Zaidi KS, Akbar R. 2023. Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems. PeerJ Computer Science 9:e1552 https://doi.org/10.7717/peerj-cs.1552 - 2023 - в издания, индексирани в Scopus или Web of Science
    10. H. A. Abdullah Abdulwali, M. H. Saleh Al-Humaidi, H. Z. Abdullah Al-Asri, A. F. Mansour Al-saidi and A. A. Al-Himiary, "Intrusions Detection System Using Machine Learning Algorithms," 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA), Taiz, Yemen, 2023, pp. 1-8, doi: 10.1109/eSmarTA59349.2023.10293386 - 2023 - в издания, индексирани в Scopus или Web of Science
    11. M. Meena and R. Kumar Tiwari, "Optimum Analysis of Imbalanced Network for Intrusion Detection using LSTM Convolution Technique," 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2023, pp. 1275-1279, doi: 10.1109/ICAAIC56838.2023.10140458 - 2023 - в издания, индексирани в Scopus или Web of Science
    12. Ali, A., Naeem, S., Anam, S., & Ahmed, M. M. (2022). Machine learning for intrusion detection in cyber security: Applications, challenges, and recommendations. UMT Artif. Intell. Rev, 2(2), 41-64. - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    13. Khalaf, A. O. A. H., Mohamed, R., & Raziff, A. R. A. (2024). Detection Model for Ambiguous Intrusion using SMOTE and LSTM for Network Security. Journal of Advanced Research in Applied Sciences and Engineering Technology, 39(2), 191-203. - 2024 - в издания, индексирани в Scopus или Web of Science
    14. Sri, K. S., Peddireddy, B., Madamanchi, V. B. R., & Bindu, G. H. (2022). A Comprehensive Analysis on Numerous Learning Models for Intrusion Detection for Security Conservation. Revue d'Intelligence Artificielle, 36(4). - 2022 - в издания, индексирани в Scopus или Web of Science
    15. Bansal, N., & Khan, M. A. (2022, December). PCA and Maker Methodology for Wildly Unbalanced Network Intrusion Performance Improvement. In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 713-717). 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