MICROTUBULE BASED NEURO-FUZZY NESTED FRAMEWORK FOR SECURITY OF CYBER PHYSICAL SYSTEM

Authors

  • Ankush Rai School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India
  • Jagadeesh Kannan R School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19646

Keywords:

Intrusion detection, artificial intelligence, machine learning

Abstract

Network and system security of cyber physical system is of vital significance in the present information correspondence environment. Hackers and network intruders can make numerous fruitful endeavors to bring crashing of the networks and web services by unapproved interruption. Computing systems connected to the Internet are stood up to with a plenty of security threats, running from exemplary computer worms to impart drive by downloads and bot networks. In the most recent years these threats have achieved another nature of automation and sophistication, rendering most defenses inadequate. Ordinary security measures that depend on the manual investigation of security incidents and attack advancement intrinsically neglect to give an assurance from these threats. As an outcome, computer systems regularly stay unprotected over longer time frames. This study presents a network intrusion detection based on machine learning as a perfect match for this issue, as learning strategies give the capacity to naturally dissect data and backing early detection of threats. The results from the study have created practical results so far and there is eminent wariness in the community about learning based defenses. Machine learning based Intrusion Detection and Network Security Systems are one of these solutions. It dissects and predicts the practices of clients, and after that these practices will be viewed as an attack or a typical conduct.

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References

W. Lee and J. Salvatore, Mining audit data to build intrusion detection models,†Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, pp. 66-72, 1998.

T. Lunt, Detecting intruders in computer systems,†Proceedings of auditing and computer technology conference, pp. 23-30, 1999.

J. Ryan, M. Lin and R. Miikkulainen, Intrusion detection with neural networks. In: Advances in neural information processing systems,†vol. 10, MIT Press, 1998.

S. Bridges, and R. Vaughn, Fuzzy data mining and genetic algorithms applied to intrusion detection,†Proceedings of the national information systems security conference, pp. 8-15, 2000.

S. Hofmeyr, S. Forrest and A. Somayaji, Intrusion detection using sequences of system calls,†Journal of Computer Security, vol. 6, pp.151-180, 1998.

J. Balasubramaniyan, J. Fernandezm, D. Isacoff D and E. Spafford, An architecture for intrusion detection using autonomous agents,†Proceedings of the annual computer security applications conference, pp. 13-24, 1998.

M. Crosbie, Applying genetic programming to intrusion detection,†Proceedings of AAAI fall symposium series, pp. 45-52, 1995.

K. Sequeira and M. Zaki, ADMIT: anomaly-base data mining for intrusions,†Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, pp. 45-56, 2002.

J. Gomez, D. Dasgupta and O. Nasraoui, Complete expression trees for evolving fuzzy classifiers systems with genetic algorithms and application to network intrusion detection,†Proceedings of the NAFIPS-FLINT joint conference, pp. 469-474, 2002.

Rai, Ankush. "Secure Two Party Computation." Journal of Advances in Shell Programming1.2 (2014): 5-6.

Rai, Ankush, and Sakkaravarthi Ramanathan. " DISTRIBUTED LEARNING IN NETWORKED CONTROLLED CYBER PHYSICAL SYSTEM." International Journal of Pharmacy and Technology 8 (3) (2016), 18537-18546.

Rai, Ankush. "Unsupervised Probabilistic Debugging." Recent Trends in Programming languages 1.2, 3 (2015): 14-16.

Published

01-04-2017

How to Cite

Rai, A., and J. K. R. “MICROTUBULE BASED NEURO-FUZZY NESTED FRAMEWORK FOR SECURITY OF CYBER PHYSICAL SYSTEM”. Asian Journal of Pharmaceutical and Clinical Research, vol. 10, no. 13, Apr. 2017, pp. 230-4, doi:10.22159/ajpcr.2017.v10s1.19646.

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Original Article(s)