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).