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Reseach Article

Cyber Security Approach in Web Application using SVM

by Chandrapal Singh Dangi, Ravindra Gupta, Gajendra Singh Chandel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 20
Year of Publication: 2012
Authors: Chandrapal Singh Dangi, Ravindra Gupta, Gajendra Singh Chandel
10.5120/9231-3796

Chandrapal Singh Dangi, Ravindra Gupta, Gajendra Singh Chandel . Cyber Security Approach in Web Application using SVM. International Journal of Computer Applications. 57, 20 ( November 2012), 30-34. DOI=10.5120/9231-3796

@article{ 10.5120/9231-3796,
author = { Chandrapal Singh Dangi, Ravindra Gupta, Gajendra Singh Chandel },
title = { Cyber Security Approach in Web Application using SVM },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 20 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number20/9231-3796/ },
doi = { 10.5120/9231-3796 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:59.593318+05:30
%A Chandrapal Singh Dangi
%A Ravindra Gupta
%A Gajendra Singh Chandel
%T Cyber Security Approach in Web Application using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 20
%P 30-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Internet is open source for web access like for the purpose of railway reservation, online banking, online fees submission etc. Security concern is the most threatening topic for users about their confidential information's storage. various security designing and algorithms has been designed to impose secure environment for user but still malicious activities , codes, algorithms, design are acting on web application to create abnormal behavior for web usage or to steal confidential, secure information for the intension of unauthorized access ,illegitimate access , access for destroying or altering the contents. Attacker's performs Site phishing, Dos attacks, pattern recognition for brute force attack etc, by using several hit and trial or input capturing methods, or by providing capturing codes or IP packets into web contents. Here in the proposed work a technique of detecting malicious Socket address (IP Address and port no. ) has been presented, which detects and blocks if any suspicious cases are found and passes the contents to concern user. Here we use SVM technique for classification, detection and prediction of Blacklisted IP addresses and blacklisted port's addresses. The proposed algorithm provides accuracy of 96. 99% and which is the best among the present systems. It is light weight system and easy to implement on existing applications.

References
  1. "A Survey of Cyber Attack Detection Systems" Shailendra Singh and Sanjay Silakari-IJCSNS International Journal of Computer Science and Network Security, VOL. 9 No. 5, May 2009
  2. The Austin Forum on Science, Technology & Society Cybersecurity Today:Trends, Risk Mitigation & Research" http://www. austinforum. org/presentations/cybersecurity. pdf"
  3. Vipin Das 1, Vijaya Pathak 2, Sattvik Sharma 3,Sreevathsan 4,MVVNS. Srikanth 5,Gireesh Kumar T,? Network Intrusion Detection System Based On Machine Learning Algorithms?, IJCSIT, Vol 2, No 6, December 2010.
  4. Ste. Zanero and Sergio M. Savaresi, "Unsupervised learning techniques for an intrusion detection system," in Proceedings of the 2004 ACM symposiumon Applied computing, pp. 412–419, Nicosia, Cyprus, Mar. 2004. ACM Press.
  5. H. Gunes Kayacik, A. Nur Zincir-Heywood, and Malcolm I. Heywood, "On the capability of an som based intrusion detection system," in Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 1808–1813. IEEE.
  6. ] J. Z. Lei and Ali Ghorbani, "Network intrusion detection using an improved competitive learning neural network," in Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR04), pp. 190–197. IEEE-Computer Society, IEEE, May 2004.
  7. Liberios VOKOROKOS et. al, "Intrusion detection system using self organizing map", Acta Electrotechnica et Informatica , Vol. 6 No. 1, pp. 1-6, 2006.
  8. Srilatha Chbrolu, Ajit Abraham, Johnson P. Thomas " Featrue deduction and ensemble design of intrusion detection systems" Computer Security, Elsevier 2004.
  9. Gopi K. Kuchimanchi, Vir V. Phoha, Kiran S. Balagani, Shekhar R. Gaddam "Dimension Reduction Using Feature Extraction Methods for Real-time Misuse Detection Systems" Proceedings of the workshop on Information Assurance and Security, US Military Academy, West Point, NY.
  10. S. Selvan, V. Venkatachalam "Performance comparison of intrusion detection system classifiers using various feature reduction techniques" International Journal of Simulation vol. 9 no. 1. 2007.
  11. Liu Yi-hung, Chen Yen-ting, face recognition using total margin based adaptive fuzzy support vector machines. IEEE Transactions on Neural Networks, 18(1): 178-192.
  12. Xiong, Sheng-Wu, Liu Hong-bing, Niu Xiao-xiao, Fuzzy support vector machines based on FCM clustering. Proceddings of the fourth international conference on
  13. Machine Learning and Cybernetics, Guangzhou, China, Aug 18-21, IEEE, p. 2608-2613, 2005.
  14. A. K. Ghosh and A. Schwartzbard. "A study in Using Neural Networks for Anomaly and Misuse detection" Proceeding of the 8th USENIX Security Symposium, pp. 23-36. Washington, D. C. US.
  15. Mukkamala S. , Sung AH, Abraham A. Modeling Inrusion Detection Systems Using linear genetic programming approach, The 17th international conference on industrial & engineering applications of artificial intelligence and expert systems, innovation in applied artificial intelligence.
  16. W. Lee, S. J. Stolfo and K. Mok. Data mining in work flow environments: Experience in intrusion detection, Proceedings of the Conference on Knowledge Discovery and Data Mining (KDD-99), 1999.
  17. Snehal A. Mulay, P. R. Devale, G. v. Garje," Intrusion Detection System using Support Vector Machine and Decision Tree", International Journal of Computer Applications (0975 - 8887) Volume 3 - No. 3, June 2010
  18. Shailendra Singh Member, IEEE, IAENG, Sanjay Agrawal, Murtaza,A. Rizvi and Ramjeevan Singh Thakur, "Improved Support Vector Machine for Cyber Attack Detection", Proceedings of The World Congress on Engineering and Computer Science 2011 Vol I WCECS 2011, October 19-21, 2011, San Francisco, USA
  19. Hoa Dinh Nguyen, Qi Cheng," An Efficient Feature Selection Method For Distributed Cyber Attack Detection and Classification",978•1-4244-9848•2/11 $26. 00©2011 IEEE
  20. Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker ,? Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs?, KDD'09, June 28–July 1, 2009, Paris, France.
  21. Technical Report: Predicting future attacks http://www. ece. uci. edu/~athina/PAPERS/dshield-analysis-tr. pdf
  22. Dshield data set"http://www. dshield. org/feeds_doc. html"
Index Terms

Computer Science
Information Sciences

Keywords

Blacklisted IP Blacklisted Port Blacklisted Socket Malicious URL Cyber Attack SVM