Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Jayanthi, J. Gnana
- JAA IDS - Framework Design For An Efficient Intrusion Detection System
Abstract Views :104 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Bharathidasan University, IN
1 Department of Computer Science, Bharathidasan University, IN
Source
ICTACT Journal on Communication Technology, Vol 12, No 4 (2021), Pagination: 2573-2576Abstract
Network security has become a very hot research area as its importance is heavily realized in various fields. Various mechanisms and tools are available to support this. But they do not meet the challenges imposed by fast growing technologies. Massive amounts of high dimensional data are one of the challenges. Data with a large number of features is entering and moving around the network. The Intrusion Detection System is a new mechanism that faces this challenge with the support of data mining and feature selection. In data mining, the ensemble is more preferable than a single method. In an ensemble, during the testing phase, all base classifiers are treated equally and individually participate and vote. To take a final decision, some extra effort has to be made. All these increase computation effort and time. To overcome these, this research paper proposes a new framework for intrusion detection systems using the Auto Bi-Level (ABL) classification technique with Double Filtering Fine Tuning–Ensemble Hybrid method.Keywords
Network Security, Features selection, Data Mining, Intrusion Detection System, Ensemble
References
- Tarfa Hamed, Rozita Dara and Stefan C. Kremer, “An Accurate, Fast Embedded Feature Selection for SVMs”, Proceedings of 13th International Conference on Machine Learning and Applications, pp. 135-140, 2014.
- Z. Xue-qin, G. Chun-Hua and L. Jia-jun, “Intrusion Detection System Based on Feature Selection and Support Vector Machine”, Proceedings of International Conference on Communications and Networking, pp. 1-5, 2006.
- A. Shadi, M. Ljawarneh and Muneer Bani Yasin, “AnomalyBased Intrusion Detection System through Feature Selection Analysis and Building Hybrid Efficient Model”, Journal of Computational Science, Vol. 23, No. 1, pp. 1-10, 2017.
- T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-inMiddle Attack”, Wireless Personal Communications, Vol. 113, pp. 1-17, 2021.
- T. Karthikeyan and K. Praghash, “Data Privacy Preservation and Trade-off Balance Between Privacy and Utility Using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, Vol. 116, pp. 1-16, 2021.
- R. Manikandan and M. Ramkumar, “Design of Autonomous Production using Deep Neural Network for Complex Job”, Materials Today: Proceedings, Vol. 4, pp. 1-12, 2021.
- Z. Xue-Qin, G. Chun-Hua and L. Jia-Jun, “Intrusion Detection System Based on Feature Selection and Support Vector Machine”, Proceedings of 1st International Conference on Communications and Networking, pp. 1-5, 2006.
- M.C. Rekha Preethi and R. Chetan, “Least Square Support Vector Machine based IDS, using Feature Selection Algorithm”, International Journal of Emerging Trends and Technology in Computer Science, Vol. 6, No. 3, pp. 64-68, 2017.
- Suleman Khan, Joseph H. Anajemba, Mohit Mittal, Mamdouh Alenezi and Mamoun Alazab, “The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems”, Sensors, Vol. 20, No. 9, pp. 255-265, 2020.
- N. Pham, E. Foo, S. Suriadi, H. Jeffrey and H. Lahza, “Improving Performance of Intrusion Detection System using Ensemble Methods and Feature Selection”, Proceedings of Australasian Multiconference on Computer Science Week, pp. 1-6, 2018.
- Fadi Salo, Ali Bou Nassif and Aleksander Essex, “Dimensionality Reduction with IG-PCA and Ensemble Classifier for Network Intrusion Detection”, Computer Networks, Vol. 148, pp. 164-175, 2019.
- L. Li, Y. Yu, S. Bai, Y. Hou and X. Chen, “An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and $k$ -NN”, IEEE Access, Vol. 6, pp. 12060-12073, 2018.
- Yuyang Zhou, Guang Cheng, Shanqing Jiang and Mian Dai, “An Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier”, Journal of Lates Files, Vol. 14, No. 8, pp. 1-13, 2019.
- Wei-Chao Lin, Shih-Wen Ke and Chih-Fong Tsai, “CANN: An Intrusion Detection System based on Combining Cluster Centers and Nearest Neighbors”, Knowledge Based Systems, Vol. 78, pp. 13-21. 2015.
- W. Du, Z. Cao, T. Song, Y. Li and Y. Liang, “A Feature Selection Method based on Multiple Kernel Learning with Expression Profiles of Different Types”, BioData Mining, Vol. 10, No. 1, pp. 1-16, 2017.