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Designing Fuzzy Rule Based Expert System for Cyber Security

Year 2012, Volume: 1 Issue: 1, 13 - 19, 10.04.2012

Abstract

The state of cyber security has begun to attract more attention and interest outside the community of computer security experts. Cyber security is not a single problem, but rather a group of highly different problems involving different sets of threats. Fuzzy Rule based system for cyber security is a system consists of a rule depository and a mechanism for accessing and running the rules. The depository is usually constructed with a collection of related rule sets. The aim of this study is to develop a fuzzy rule based technical indicator for cyber security with the use of expert system. Rule based systems employ fuzzy rule to automate complex processes. Common cyber threats assumed for cyber experts are used as linguistic variables in this paper.

References

  • R. Chandia, J. Gonzalez, T. Kilpatrick, M. Papa, S. Shenoi, “Security strategies for SCADA networks,” in: Proceeding of the First Annual IFIP Working Group 11.10 International Conference on Critical Infrastructure Protection, Hampshire, USA, Mar. 19-21, 2007. College, Hanover, New
  • N. Fovino, M. Masera, “Through the description of attacks: a multidimensional view”, in: Proceeding of the 25th International Conference on Computer Safety, Reliability and Security, Gdansk, Poland, Sep. 26-29, 2006.
  • R. Shanmugavadivu, “Network Intrusion Detection System Using Fuzzy Logic”, Indian Journal of Computer Science and Engineering (IJCSE), vol.2, 1, pp. 101-111, 2011.
  • S. M. Bridges, and R. B.Vaughn, “Fuzzy Data Mining And Detection”, In Proceedings of the National Information Systems Security Conference (NISSC), Baltimore, MD, 2000, pp.16-19. Applied to Intrusion
  • J.T. Yao, S.L. Zhao, and L.V. Saxton, “A Study On Fuzzy Intrusion Detection”, In Proceedings of the Data Mining, Intrusion Detection, Information Assurance, And Data Networks Security, SPIE, Vol. 5812, Orlando, Florida, USA, 2005, pp. 23-30.
  • S. Mukkamala, G. Janoski, A. Sung, “Intrusion detection: support vector networks.” In: Proceedings of the IEEE International Joint Conference on Neural Networks (ANNIE), St. Louis, MO, 2002, pp. 1702-1707. neural
  • Y. Yu, and H. Hao, “An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm”, Journal of Software, Vol.18, No.6, pp.1369-1378, June 2007.
  • J. Cannady, “Artificial Neural Networks for Misuse Detection”, in Proceedings of the ’98 National Information System Security Conference (NISSC’98), 1998, pp. 443-456.
  • W. Lee, S. Stolfo, and K. Mok, “A Data Mining Framework for Building Intrusion Detection Model”, In Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, 1999, pp. 120-132.
  • J. Luo, and S. M. Bridges, “Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection”, International Journal of Intelligent Systems, Vol. 15, No. 8, pp. 687-704, 2000.
  • C. Wilson, “Computer Attack and Cyber Terrorism: Vulnerabilities and Policy Issues for Congress”, CRS Report for Congress, Oct. 17, 2003.
  • N. Fovino, M. Masera, “A service oriented approach to the assessment of infrastructure security”, in: Proceeding of the First Annual IFIP Working Group 11.10 International Conference on Critical Infrastructure Protection, Hampshire, USA, Mar. 19-21, 2007. College, Hanover, New
  • S.M.Furnel and M.J.Warren, “Computer Hacking and Cyber Terrorism: The Real Threats in the New Millennium?”, Computers & Security, vol.18, pp.28- 34,1999.
  • L. Pietre-Cambacedes, T. Kropp, J.Weiss, and R. Pellizzonni, “Cybersecurity standards for the electric power industry-A survival kit,” in CIGRÉ Paris Session, 2008, D2-217.
  • R. P. Evans, R. C. Hill, and J. G. Rodriquez, “A Comparison of CrossSector Cyber Security Standards Idaho National Laboratories”, Idaho National Labs Rep. INL/EXT-05-00656, 2005.
  • M. Ferris, “New Email Security Infrastructure”, Proceeding of New security Paradigms Workshop, Aug. 3-5, 1994, pp. 20-27.
  • M. Majdalawieh, F. Parisi-Presicce, D. Wijesekera, “Distributed network protocol security (DNPSec) security framework”, in: Proceedings of the 21st Annual Computer Security Applications Conference, Tucson, Arizona, Dec. 5-9, 2005.
  • A. Abraham, C. Grosan, C. Martin-Vide, “Evolutionary design of intrusion detection programs.” International Journal of Network Security, vol. 4(3), pp.328-339, 2007.
  • W. Chimphlee, A.H. Abdullah, M.N. Sap, S. Srinoy, and S. Chimphlee, “Anomaly-based intrusion detection using fuzzy rough clustering.” In Proceedings of the international technology (ICHIT’06), 2006, pp. 320-334. on hybrid information
  • L. Khan, M. Awad, and B. Thuraisingham. “A new intrusion detection system using support vector machines and hierarchical clustering”, The International Journal on Very Large Data Bases, vol. 16(4), pp.507– 521, 2007.
  • A.N. Toosi, M. Kahani, “A new approach to intrusion detection based on an evolutionary soft computing model Communications, vol.30, pp. 2201-221, 2007. classifiers. Computer
  • A. Tajbakhsh, M. Rahmati, A. Mirzaei, "Intrusion detection using fuzzy association rules", Applied Soft Computing, Vol: 9, No: 2, pp. 462-469, 2009.
  • B. Shanmugam, N. B. Idris, "Improved Intrusion Detection System Using Fuzzy Logic for Detecting Anamoly and Misuse Type of Attacks", in Proceedings of the International Conference of Soft Computing and Pattern Recognition, 2009, pp: 212-217.
  • O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, L. Magdalena, “Ten years of genetic fuzzy systems: current framework and new trends”, Fuzzy Sets and Systems, vol.141, no.1, pp. 5–31, 2004.
  • L.A. Zadeh, “Fuzzy sets”, Information Control, vol.8, pp.338-353,1965.
  • E.H. Mamdani, and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, Int. J. Man-Mach. Stud., vol.7, pp.1-13, 1975.
  • J. Lu, G. Zhang, D. Ruan, “Multi-Objective Group Decision Making: Methods, Software and Applications with Fuzzy Set Techniques”, Imperial College Press, London, 2007.
  • J.C. Giarratano and G. Riley, “Expert systems principles and programming”, MA, USA: PWS-KENT Publishing Company, 1989.
  • N.J. Nilsson, “Principles of Artificial Intelligence”, Palo Alto, CA. Tioga, 1980.
  • M. Schneider, G. Langholz, A. Kandel, and G. Chew, “Fuzzy Expert System Tools”, John Wiley & Sons, USA, 1996.
  • J. J. Prichard and L. E. MacDonald, “Cyber Terrorism: A Study of the Extent of Coverage in Computer Security Textbooks”, Journal of Information Technology Education, Vol. 3, 2004.
  • J. Moteff and P. Parfomak, “Critical Infrastructure and Key Assets: Definition and Identification”, CRS Report for Congress , Oct. 1, 2004.
  • J. A. Chandler, Security in Cyberspace: Combatting Distributed Denial of Service Attacks, , University of Ottawa Law & Technology Journal, pp.231-261, 2004.
  • W. Siler, J. J. Buckley, Fuzzy Expert Systems and Fuzzy Reasoning, New Jersey, 2005.
  • L. Medsker, J. Liebowitz, “Design and development of expert systems and neural networks”, NY, USA: McMillan College Publishing Company, 1994.
  • A. Kengpol and W. Wangananon, “The expert system for assessing customer satisfaction on fragrance notes: Using artificial neural networks”, Computer & Industrial Engineering, vol.51(4), pp.567-584, 2006.
  • H. Hellendoorn, and C. Thomas, “Defuzzification in fuzzy controllers”, Int. Fuzzy Syst., vol.1, pp.109-123, 1993.
  • M. Fasanghari, F.H. Roudsari, “The fuzzy evaluation of e-commerce customer satisfaction”, World Applied Sciences Journal, vol.4(2), pp.164-168, 2008.
  • A.A. Gamil, R.S. El-fouly and N.M. Darwish, “Stock technical analysis using multi agent and fuzzy logic”, Proceedings of the world congress on engineering. Vol I WCE 2007, Jul. 2-4, 2007.
  • E. Giovanis, “Application of adaptive network-based fuzzy inference system in macroeconomic variables forecasting”, World Acad. Sci. Eng. Technoh, vol.64, pp.660-667, 2010.
  • H. Kwasnicka, and M. Ciosmak, “Intelligent techniques in stock analysis”, Proceedings of Intelligent Information Systems. (IIS'02), Arnetminer, 2001, pp. 195-208.
  • S. Önüt, S.S. Kara, and E. Işık, “Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company Industrial Engineering”, Expert Systems with Applications, vol.36, pp. 3887-3895, 2009.
  • M. Ganesh, “Introduction to Fuzzy Sets and Fuzzy Logic”, Phi Learning, India, 2009.
  • R. Shanmugavadivu, “Network Intrusion Detection System Using Fuzzy Logic”, Indian Journal of Computer Science and Engineering (IJCSE), vol.2, 1, pp. 101-111, 2011.
Year 2012, Volume: 1 Issue: 1, 13 - 19, 10.04.2012

Abstract

References

  • R. Chandia, J. Gonzalez, T. Kilpatrick, M. Papa, S. Shenoi, “Security strategies for SCADA networks,” in: Proceeding of the First Annual IFIP Working Group 11.10 International Conference on Critical Infrastructure Protection, Hampshire, USA, Mar. 19-21, 2007. College, Hanover, New
  • N. Fovino, M. Masera, “Through the description of attacks: a multidimensional view”, in: Proceeding of the 25th International Conference on Computer Safety, Reliability and Security, Gdansk, Poland, Sep. 26-29, 2006.
  • R. Shanmugavadivu, “Network Intrusion Detection System Using Fuzzy Logic”, Indian Journal of Computer Science and Engineering (IJCSE), vol.2, 1, pp. 101-111, 2011.
  • S. M. Bridges, and R. B.Vaughn, “Fuzzy Data Mining And Detection”, In Proceedings of the National Information Systems Security Conference (NISSC), Baltimore, MD, 2000, pp.16-19. Applied to Intrusion
  • J.T. Yao, S.L. Zhao, and L.V. Saxton, “A Study On Fuzzy Intrusion Detection”, In Proceedings of the Data Mining, Intrusion Detection, Information Assurance, And Data Networks Security, SPIE, Vol. 5812, Orlando, Florida, USA, 2005, pp. 23-30.
  • S. Mukkamala, G. Janoski, A. Sung, “Intrusion detection: support vector networks.” In: Proceedings of the IEEE International Joint Conference on Neural Networks (ANNIE), St. Louis, MO, 2002, pp. 1702-1707. neural
  • Y. Yu, and H. Hao, “An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm”, Journal of Software, Vol.18, No.6, pp.1369-1378, June 2007.
  • J. Cannady, “Artificial Neural Networks for Misuse Detection”, in Proceedings of the ’98 National Information System Security Conference (NISSC’98), 1998, pp. 443-456.
  • W. Lee, S. Stolfo, and K. Mok, “A Data Mining Framework for Building Intrusion Detection Model”, In Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, 1999, pp. 120-132.
  • J. Luo, and S. M. Bridges, “Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection”, International Journal of Intelligent Systems, Vol. 15, No. 8, pp. 687-704, 2000.
  • C. Wilson, “Computer Attack and Cyber Terrorism: Vulnerabilities and Policy Issues for Congress”, CRS Report for Congress, Oct. 17, 2003.
  • N. Fovino, M. Masera, “A service oriented approach to the assessment of infrastructure security”, in: Proceeding of the First Annual IFIP Working Group 11.10 International Conference on Critical Infrastructure Protection, Hampshire, USA, Mar. 19-21, 2007. College, Hanover, New
  • S.M.Furnel and M.J.Warren, “Computer Hacking and Cyber Terrorism: The Real Threats in the New Millennium?”, Computers & Security, vol.18, pp.28- 34,1999.
  • L. Pietre-Cambacedes, T. Kropp, J.Weiss, and R. Pellizzonni, “Cybersecurity standards for the electric power industry-A survival kit,” in CIGRÉ Paris Session, 2008, D2-217.
  • R. P. Evans, R. C. Hill, and J. G. Rodriquez, “A Comparison of CrossSector Cyber Security Standards Idaho National Laboratories”, Idaho National Labs Rep. INL/EXT-05-00656, 2005.
  • M. Ferris, “New Email Security Infrastructure”, Proceeding of New security Paradigms Workshop, Aug. 3-5, 1994, pp. 20-27.
  • M. Majdalawieh, F. Parisi-Presicce, D. Wijesekera, “Distributed network protocol security (DNPSec) security framework”, in: Proceedings of the 21st Annual Computer Security Applications Conference, Tucson, Arizona, Dec. 5-9, 2005.
  • A. Abraham, C. Grosan, C. Martin-Vide, “Evolutionary design of intrusion detection programs.” International Journal of Network Security, vol. 4(3), pp.328-339, 2007.
  • W. Chimphlee, A.H. Abdullah, M.N. Sap, S. Srinoy, and S. Chimphlee, “Anomaly-based intrusion detection using fuzzy rough clustering.” In Proceedings of the international technology (ICHIT’06), 2006, pp. 320-334. on hybrid information
  • L. Khan, M. Awad, and B. Thuraisingham. “A new intrusion detection system using support vector machines and hierarchical clustering”, The International Journal on Very Large Data Bases, vol. 16(4), pp.507– 521, 2007.
  • A.N. Toosi, M. Kahani, “A new approach to intrusion detection based on an evolutionary soft computing model Communications, vol.30, pp. 2201-221, 2007. classifiers. Computer
  • A. Tajbakhsh, M. Rahmati, A. Mirzaei, "Intrusion detection using fuzzy association rules", Applied Soft Computing, Vol: 9, No: 2, pp. 462-469, 2009.
  • B. Shanmugam, N. B. Idris, "Improved Intrusion Detection System Using Fuzzy Logic for Detecting Anamoly and Misuse Type of Attacks", in Proceedings of the International Conference of Soft Computing and Pattern Recognition, 2009, pp: 212-217.
  • O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, L. Magdalena, “Ten years of genetic fuzzy systems: current framework and new trends”, Fuzzy Sets and Systems, vol.141, no.1, pp. 5–31, 2004.
  • L.A. Zadeh, “Fuzzy sets”, Information Control, vol.8, pp.338-353,1965.
  • E.H. Mamdani, and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, Int. J. Man-Mach. Stud., vol.7, pp.1-13, 1975.
  • J. Lu, G. Zhang, D. Ruan, “Multi-Objective Group Decision Making: Methods, Software and Applications with Fuzzy Set Techniques”, Imperial College Press, London, 2007.
  • J.C. Giarratano and G. Riley, “Expert systems principles and programming”, MA, USA: PWS-KENT Publishing Company, 1989.
  • N.J. Nilsson, “Principles of Artificial Intelligence”, Palo Alto, CA. Tioga, 1980.
  • M. Schneider, G. Langholz, A. Kandel, and G. Chew, “Fuzzy Expert System Tools”, John Wiley & Sons, USA, 1996.
  • J. J. Prichard and L. E. MacDonald, “Cyber Terrorism: A Study of the Extent of Coverage in Computer Security Textbooks”, Journal of Information Technology Education, Vol. 3, 2004.
  • J. Moteff and P. Parfomak, “Critical Infrastructure and Key Assets: Definition and Identification”, CRS Report for Congress , Oct. 1, 2004.
  • J. A. Chandler, Security in Cyberspace: Combatting Distributed Denial of Service Attacks, , University of Ottawa Law & Technology Journal, pp.231-261, 2004.
  • W. Siler, J. J. Buckley, Fuzzy Expert Systems and Fuzzy Reasoning, New Jersey, 2005.
  • L. Medsker, J. Liebowitz, “Design and development of expert systems and neural networks”, NY, USA: McMillan College Publishing Company, 1994.
  • A. Kengpol and W. Wangananon, “The expert system for assessing customer satisfaction on fragrance notes: Using artificial neural networks”, Computer & Industrial Engineering, vol.51(4), pp.567-584, 2006.
  • H. Hellendoorn, and C. Thomas, “Defuzzification in fuzzy controllers”, Int. Fuzzy Syst., vol.1, pp.109-123, 1993.
  • M. Fasanghari, F.H. Roudsari, “The fuzzy evaluation of e-commerce customer satisfaction”, World Applied Sciences Journal, vol.4(2), pp.164-168, 2008.
  • A.A. Gamil, R.S. El-fouly and N.M. Darwish, “Stock technical analysis using multi agent and fuzzy logic”, Proceedings of the world congress on engineering. Vol I WCE 2007, Jul. 2-4, 2007.
  • E. Giovanis, “Application of adaptive network-based fuzzy inference system in macroeconomic variables forecasting”, World Acad. Sci. Eng. Technoh, vol.64, pp.660-667, 2010.
  • H. Kwasnicka, and M. Ciosmak, “Intelligent techniques in stock analysis”, Proceedings of Intelligent Information Systems. (IIS'02), Arnetminer, 2001, pp. 195-208.
  • S. Önüt, S.S. Kara, and E. Işık, “Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company Industrial Engineering”, Expert Systems with Applications, vol.36, pp. 3887-3895, 2009.
  • M. Ganesh, “Introduction to Fuzzy Sets and Fuzzy Logic”, Phi Learning, India, 2009.
  • R. Shanmugavadivu, “Network Intrusion Detection System Using Fuzzy Logic”, Indian Journal of Computer Science and Engineering (IJCSE), vol.2, 1, pp. 101-111, 2011.
There are 44 citations in total.

Details

Primary Language English
Subjects Applied Mathematics
Journal Section Articles
Authors

Kerim Goztepe

Publication Date April 10, 2012
Submission Date January 30, 2016
Published in Issue Year 2012 Volume: 1 Issue: 1

Cite

IEEE K. Goztepe, “Designing Fuzzy Rule Based Expert System for Cyber Security”, IJISS, vol. 1, no. 1, pp. 13–19, 2012.