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Predicate based Algorithm for Malicious Web Page Detection using Genetic Fuzzy Systems and Support Vector Machine
by
S. Chitra,
K. S. Jayanthan,
S. Preetha,
R. N. Uma Shankar
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 10 |
| Year of Publication: 2012 |
| Authors: S. Chitra, K. S. Jayanthan, S. Preetha, R. N. Uma Shankar |
10.5120/5000-7277
|
S. Chitra, K. S. Jayanthan, S. Preetha, R. N. Uma Shankar . Predicate based Algorithm for Malicious Web Page Detection using Genetic Fuzzy Systems and Support Vector Machine. International Journal of Computer Applications. 40, 10 ( February 2012), 13-19. DOI=10.5120/5000-7277
@article{
10.5120/5000-7277,
author = {
S. Chitra,
K. S. Jayanthan,
S. Preetha,
R. N. Uma Shankar
},
title = { Predicate based Algorithm for Malicious Web Page Detection using Genetic Fuzzy Systems and Support Vector Machine },
journal = {
International Journal of Computer Applications
},
issue_date = { February 2012 },
volume = { 40 },
number = { 10 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = {
13-19
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume40/number10/5000-7277/
},
doi = { 10.5120/5000-7277 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:42.464553+05:30
%A S. Chitra
%A K. S. Jayanthan
%A S. Preetha
%A R. N. Uma Shankar
%T Predicate based Algorithm for Malicious Web Page Detection using Genetic Fuzzy Systems and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 10
%P 13-19
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In the era of internet, users are keen to discover more in the web. As the number of web pages increases day-by-day malicious web pages are also increasing proportionally. This paper focus on detecting maliciousness in a web page using genetically evolved fuzzy rules. The above formed rules are filtered by Support Vector Machine and finally storing the result in a symbolic knowledge base, with appropriate weightage for each rule. This provides an insight to symbolic and non-symbolic intelligence in malicious web page detection.
References
- A.Ikinci, T. Holz and F. Freiling, Monkey-Spider: Detecting Malicious Websites with Low-Interaction Honeyclients, Sicherheit, Saarbruecken, 2008.
- C. Seifert, I. Welch and P. Komisarczuk, HoneyC - The Low- Interaction Client Honeypot, NZCSRSC, Hamilton, 2007.
- C. Seifert, I. Welch and P. Komisarczuk, Identification of Malicious Web Pages with Static Heuristics, Telecommunication Networks and Applications Conference, 2008. ATNAC 2008. Australasian, 2008.
- Chia-FengJuang, Shih-Hsuan Chin and Shu-Wew Chang, A self organizing TS-Type Fuzzy Network with Support Vector Learning and its Application to Classification problems, IEEE transactions on Fuzzy Systems, vol. 15, no.5, 2007.
- Claudio Vaucheret, Sergio Gaudarrama and Susana Munoz, Fuzzy prolog :a simple general implementation using CLP(R).
- Davis, La Jolla, CA: Morgan Kaufmann, Adapting operator probabilities in genetic algorithms, Proceedings of the Third International Conference on Genetic Algorithms, 60-69,1989
- E. Moshchuk, T. Bragin, S. D. Gribble and H. M. Levy, A crawlerbased study of spyware on the Web, (2006).
- Francisco Herrera, Genetic Fuzzy systems: A state of Art and new trends.
- Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions by Francisco Herrera on International Journal of Computational Intelligence Research. ISSN 0973-1873 Vol.1, No.1 (2005), pp. 59-67.
- Introduction to Fuzzy Systems, Neural Network and Genetic Algorithms by Hideyuki TAKAGI in Intelligent Systems: Fuzzy Logic, Neural Network and Genetic Algorithms Ch.1 pp.1-33 by D.Ruan, Kluwer Academic Publishers, September 1997.
- J. Ma, L. K. Saul, S. Savage and G. M. Voelker, Beyond blacklists: learning to detect malicious web sites from suspicious URLs, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Paris, France, 2009.
- Jorge Casillas, Brian Carse and Larry Bull, Fuzzy-XCS: A Michigan Genetic Fuzzy System, IEEE Transactions on Fuzzy Systems, vol. 15, no. 4, 2007.
- Kowaiczyk, R. On numerical and linguistic quanti_cation in linguistic approximation, IEEE International Conference on Systems, Man, and Cybernetics, 326{331.}, 1999
- L. Bin, H. Jianjun, L. Fang, W. Dawei, D. Daxiang and L. Zhaohui, Malicious Web Pages Detection Based on Abnormaingl Visibility Recognition, E-Business and Information System Security, 2009. EBISS ‘09. International Conference on, 2009, pp. 1-5.
- L. Shih-Fen, H. Yung-Tsung, C. Chia-Mei, J. Bingchiang and L. Chi- Sung, Malicious Webpage Detection by Semantics-Aware Reasoning, Intelligent Systems Design and Applications, 2008. ISDA ‘08. Eighth International Conference on, 2008.
- L.A. Zadeh, Fuzzy Sets. Information and Control 8, 338{353}, 1965.
- N. Provos, P. Mavrommatis, M. Abu and R. F. Monrose, All your iframes point to us, Google Inc, 2008.
- O.Cordon, F. Gomide, F.Herrerea, F.Hoffman and L. Magdalena “Ten years of genetic fuzzy systems: Current Framework and new trends”, Fuzzy Sets Syst., vol. 141, no. 1, pp. 5-31, 2004
- O.Cordon, F.Herrerea, F.Hoffman and L. Magdalena, Genetic Fuzzy Systems. Evolutionary tuning and learning of Fuzzy Knowledge bases, ser. Advances in Fuzzy Systems – Applications and Theory Series. Singapore: World Scientific, 2001, vol. 19
- On Advantages of Scheduling using Genetic Fuzzy Systems by Carsten Franke, Joachim Lepping and Uwe Schwiegelshohn
- Ossi Nykanen, An Approach to Logic Programming with Type-1 Fuzzy Models Using Prolog, IADIS International Conference Applied Computing, 2006.
- P. Liu and X. Wang, Identification of Malicious Web Pages by Inductive Learning, Proceedings of the International Conference on Web Information Systems and Mining, Springer-Verlag, Shanghai, China, 2009.
- P. Niels, R. Moheeb Abu and M. Panayiotis, Cybercrime 2.0: When the Cloud Turns Dark, Queue, 7 (2009), pp. 46-47.
- S. Xiaoyan, W. Yang, R. Jie, Z. Yuefei and L. Shengli, Collecting Internet Malware Based on Client-side Honeypot, Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for, 2008, pp. 1493-1498.
- Spears, W. M. & Anand, V., Charlotte, NC: Springer-Verlag, A study of crossover operators in genetic programming. Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems, 409-418, 1991.
- Syswerda, G., Vail, CO: Morgan Kaufmann, Simulated crossover in genetic algorithms. Proceedings of the Foundations of Genetic Algorithms Workshop, 1992
- T.P. Martin, J.F. Baldwin, B.W. Pilsworth, The Implementation of Fprolog – A Fuzzy Prolog Interpreter. Fuzzy Sets and Systems 23, 119 {129}, 1985.
- Technical Report on Ten Lecturers on Genetic Fuzzy Systems by Ulrich Bodenhofer, Francisco Herrera. Revised version of lecturer notes from “Preprints of the International Summer School: Advanced Control-Fuzzy, Neural, Genetic”, R.Mesiar, Ed.Slovak technical University Bratislava 1997. Pp. 1-69, ISBN.
- Time Complexity Ananlysis of Genetic – Fuzzy System for disease diagnosis by Ephzibah E.P. in Advanced Computing: An International Journal) ACIJ), Vol.2, No.4, July 2011.
- Toshinori Munakata, Notes on implementing Fuzzy sets in Prolog, Fuzzy Sets and System, 1998.
- V.Vapnik, The Nature of Statistical Learning Theory. New York: Springer- Verlag, 1995.
- Van Lam Le, Ian Welch, Xiaoying Gao, Peter Komisarczuk, Two-stage classification model to detect malicious web page, International Conference on Advanced Information Networking and Applications,2011.
- Y.-M. Wang, D. Beck, X. Jiang and R. Roussev, Automated Web Patrol with Strider HoneyMonkeys: Finding Web Sites that Exploit Browser Vulnerabilities, IN NDSS (2006).
- Y.-T. Hou, Y. Chang, T. Chen, C.-S. Laih and C.-M. Chen, Malicious web content detection by machine learning, Expert Systems with Applications, In Press, Corrected Proof (2009).
Index Terms
Computer Science
Information Sciences
Keywords