CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Cyber-Attack Classification using Improved Ensemble Technique based on Support Vector Machine and Neural Network

by Bhavna Dharamkar, Rajni Ranjan Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 11
Year of Publication: 2014
Authors: Bhavna Dharamkar, Rajni Ranjan Singh
10.5120/18115-9346

Bhavna Dharamkar, Rajni Ranjan Singh . Cyber-Attack Classification using Improved Ensemble Technique based on Support Vector Machine and Neural Network. International Journal of Computer Applications. 103, 11 ( October 2014), 1-7. DOI=10.5120/18115-9346

@article{ 10.5120/18115-9346,
author = { Bhavna Dharamkar, Rajni Ranjan Singh },
title = { Cyber-Attack Classification using Improved Ensemble Technique based on Support Vector Machine and Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 11 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number11/18115-9346/ },
doi = { 10.5120/18115-9346 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:14.940992+05:30
%A Bhavna Dharamkar
%A Rajni Ranjan Singh
%T Cyber-Attack Classification using Improved Ensemble Technique based on Support Vector Machine and Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 11
%P 1-7
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cyber-attack classification and detection process is based on the fact that intrusive activities are different from normal system activities . Its detection is a very complex process in network security. In current network security scenario various types of cyber-attack family exist, some are known family and some are unknown one . The detection of known attack is not very difficult it generally uses either signature base approach or rule based approach, but to find out the unknown one is a challenging task. Intrusion detection is a process for this . One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using of an ensemble classification methods for intrusion detection. The paper proposes a cascaded support vector machine classifier or an improved ensemble classifier using multiple kernel function. The multiple kernel is Gaussian in nature. The graph based /neural network technique used for feature collection of different types of cyber-attack data. The proposed algorithm is very efficient in comparison of pervious method.

References
  1. Shailendra Singh, Sanjay Silakari "An Ensemble Approach for Cyber Attack Detection System: A Generic Framework" 14th ACIS, IEEE 2013. Pp 79-85.
  2. X. Li et al. , "Smart Community: An Internet of Things Application," IEEE Commun. Mag. , vol. 49, no. 11, 2011, pp. 68–75.
  3. V. Bapuji, R. Naveen Kumar2,Dr. A. Govardhan, S. S. V. N. Sarma "Soft Computing and Artificial Intelligence Techniques for Intrusion Detection System" Vol 2, No. 4, 2012, pp 24-33.
  4. Hoa Dinh Nguyen , Qi Cheng "An Efficient Feature Selection Method For Distributed Cyber Attack Detection and Classification" IEEE 2013. pp 1-6.
  5. Bimal Kumar Mishra,Hemraj Saini "Cyber Attack Classification using Game Theoretic Weighted Metrics Approach" World Applied Sciences Journal 7, 2009. Pp 206-215.
  6. Xu Li, Inria Lille, Xiaohui Liang, Xiaodong Lin, Haojin Zhu "Securing Smart Grid: Cyber Attacks,Countermeasures, and Challenges" IEEE Communications Magazine IEEE 2012. Pp 38-46.
  7. Haitao Du, Christopher Murphy, Jordan Bean, Shanchieh Jay Yang "Toward Unsupervised Classification of Non-uniform Cyber Attack Tracks" International Conference on Information Fusion 2009. Pp 1919-1925.
  8. Abhishek Jain And Ashwani Kumar Singh "Distributed Denial Of Service (Ddos) Attacks - Classification And Implications"journal of Information and Operations Management vol-3 2012. Pp 136– 140.
  9. Dewan Md. Farid, Nouria Harbi, Emna Bahri, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman "Attacks Classification in Adaptive Intrusion Detection using Decision Tree" World Academy of Science, Engineering and Technology, 2009. Pp 86-91.
  10. Chee-Wooi Ten, Govindarasu Manimaran "Cybersecurity for Critical Infrastructures:Attack and Defense Modeling "IEEE TRANSACTIONS ON SYSTEMS, vol-40 IEEE 2010. Pp 853-865.
  11. Mohammad A. Faysel , and Syed S. Haque "Towards Cyber Defense: Research in Intrusion Detection and Intrusion Prevention Systems" IJCSNS, vol-7 2010. Pp 316-325.
  12. Shailendra Singh, Sanjay Agrawal, Murtaza,A. Rizvi and Ramjeevan Singh Thakur " Improved Support Vector Machine for Cyber Attack Detection" WCECS IEEE 2011. Pp 1-6.
  13. Real-time Misuse Detection Systems, Proceedings of the IEEE on Information, 2004. [14 Vineet Richhariya , Dr. J. L. Rana ,Dr. R. C. Jain ,Dr. R. K. Pandey" Design of Trust Model For Efficient Cyber Attack Detection on Fuzzified Large Data using Data Mining techniques" IJRCCT Vol 2, Issue 3, 2013. Pp 126-132.
  14. Deepak Rathore and Anurag Jain "Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network" in International Journal of Advanced Computer Research Volume-2 Number-3 Issue-5 September-2012.
  15. M Govindarajan and RM. Chandrasekaran "Cyber-Attack Classification Using Improved Ensemble Technique Based On Support Vector Machine and Neural Network" Proceding of the World Congress on Engineering and Computer Science 2012 Vol IWCECS 2012, October 24-26, 2012, San Francisco, USA
  16. Freund, Y. and Schapire, R. (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In proceedings of the Second European Conference on Computational Learning Theory, pp 23-37.
Index Terms

Computer Science
Information Sciences

Keywords

SVM Gaussian hyper plane Euclidean distance