AN APPROACH USING ENSEMBLE CORE VECTOR MACHINES FOR NETWORK IDS
DOI:
https://doi.org/10.61841/zx8ra778Keywords:
Intrusion Detection System, core vector machine, minimum enclosing ball,, attacks, hi-square testAbstract
Past many activities carried by common man were manual. Today’s stay connected world there’s huge usage of e-services which made man’s activities online, along with these services security concerns also increased. Many researchers proposed efficient Intrusion Detection systems are in practice; but still hackers manage to attack the systems to intrude. This paper proposes an efficient intrusion detection system using Ensemble Core Vector Machine approach, where algorithms work on the basis of Minimum Enclosing Ball concept. It detects the attacks like: R2L, U2R, Probe and DoS attack. For each type of attack, a CVM classifier is modeled. KDD Cup’99 datasets are used for training and testing the classifiers. This approach uses Chi-square test for selecting the relevant features for each attack and a weighted function is applied to these features for the dimensionality reduction. The test results verify that this model achieves high efficiency in all the four attacks with less computation time compared to the existing approaches.
Downloads
References
1. P.Amudha, S.Karthik, S.Sivakumari, “Intrusion Detection Based on Core Vector Machine and Ensemble Classification Methods”, 2015 International Conference on Soft-Computing and Network Security (ICSNS -2015), Feb. 25 – 27, 2015, Coimbatore, INDIA.
2. Anna L. Buczak, ErhanGuven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection”, IEEE Communications Surveys & Tutorials,18(2), Second Quarter 2016.
3. Ye Chen et al, “Hierarchical Core Vector Machines for Network Intrusion Detection”, ICONIP 2009, Part II, LNCS 5864,520–529, 2009. Springer-Verlag Berlin Heidelberg 2009.
4. L.Dhanabal, Dr. S.P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering, 4(6), June 2015.
5. D.P.Gaikwad, Ravindra C. Thool, “Intrusion Detection System Using Bagging Ensemble Method of Machine Learning”, International Conference on Computing Communication Control and Automation, 2015, IEEE Computer Society.
6. Wei Hu and Weiming Hu, “Network-based Intrusion Detection Using Adaboost Algorithm”, Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05),IEEE Computer Society.
7. JayshreeJha, LeenaRagha, “Intrusion Detection System using Support Vector Machine”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868.
8. Wei Li, QingXia Li, “Using Naive Bayes with AdaBoost to Enhance Network Anomaly Intrusion Detection”, 2010 Third International Conference on Intelligent Networks and Intelligent Systems.
9. Mrutyunjaya Panda, ManasRanjan Patra, “Network Intrusion Detection Using Naïve Bayes”, IJCSNS International Journal Of Computer Science And Network Security, 7(12), December 2007.
10. RifkiePrimartha, BayuAdhi Tama, “Anomaly Detection using Random Forest: A Performance Revisited”, 2017 International Conference on Data and Software Engineering (ICoDSE),IEEE.
11. Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung, “Core Vector Machines: Fast SVM Training on Very Large Data Sets”, Journal of Machine Learning Research 6 (2005) 363–392.
12. Ivor W. Tsang, AndrasKocsor, James T. Kwok, “Simpler Core Vector Machines with Enclosing Balls”, Proceedings of the 24 thInternational Conference on Machine Learning, Corvallis, 911- 918, 2007.
13. Ivor W. Tsang, James T. Kwok and Pak-Ming Cheung, “ Very large SVM training using core vector machines”, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), Barbados, January 2005.
14. R.Ravinder Reddy, B.Kavya, Y Ramadevi, “A Survey on SVM Classifiers for Intrusion Detection”, International Journal of Computer Applications (0975 – 8887), 98(19), July 2014.
15. ]ShailendraSahu, B M Mehtre, “Network Intrusion Detection System Using J48 Decision Tree”, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
16. ZibusisoDewa, Leandros A. Maglaras, “Data Mining and Intrusion Detection Systems”, (IJACSA) International Journal of Advanced Computer Science and Applications, 7( 1), 2016.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
