The week's pick
Reseach Article
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