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Applications of Machine Learning in Cyber Security


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1 Department of Computer Science, RVCE, Bengaluru, India
     

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Machine learning techniques have been applied in many areas of science due to their unique properties like adaptability, scalability, and potential to rapidly adjust to new and unknown challenges. Cyber security is a fast-growing field demanding a great deal of attention because of remarkable progresses in social networks, cloud and web technologies, online banking, mobile environment, smart grid, etc. Diverse machine learning methods have been successfully deployed to address such wide-ranging problems in computer security. This paper discusses and highlights different applications of machine learning in cyber security. This study covers phishing detection, network intrusion detection, testing security properties of protocols, authentication with keystroke dynamics, cryptography, human interaction proofs, spam detection in social network, smart meter energy consumption profiling, and issues in security of machine learning techniques itself.


Keywords

Security, Machine Learning, Survey.
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  • S. Abu-Nimeh, D. Nappa, X. Wang, and S. Nair, “A Comparison of Machine Learning Techniques for Phishing Detection”, APWG eCrime Researchers Summit, October 4-5, 2007, Pittsburg, PA.

  • Anti-Phishing Working Group, “Phishing and Fraud solutions”. [Online]. Available: http://www.antiphishing.org/. [Accesses: April 4, 2013].

  • M. Wu, R. C. Miller, and S. L. Garnkel, “Do security toolbars actually prevent phishing attacks?” in Proceedings of the SIGCHI conference on Human Factors in computing systems, 2006.

  • L. F. Cranor, S. Egelman, J. Hong, and Y. Zhang, “Phinding phish: An evaluation of anti-phishing toolbars”, Technical Report CMU-CyLab-06-018, CMU, November 2006.

  • M. Chandrasekaran, K. Narayanan, and S. Upadhyaya, “Phishing email detection based on structural properties”, in NYS Cyber Security Conference, 2006.

  • W. Zhuang, Y. Ye, Y. Chen, and T. Li, “Ensemble Clustering for Internet Security Applications”, in IEEE xplore, December 17, 2012.

  • K. Lee, J. Caverlee, and S. Webb, “Uncovering social spammers: social honeypots + machine learning”, SIGIR’10, July 19-23, 2010, Geneva, Switzerland.

  • N. Lu, S. Mabu, T. Wang, and K. Hirasawa, “An Efficient Class Association Rule-Pruning Method for Unified Intrusion Detection System using Genetic Algorithm”, in IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, Issue 2, pp. 164 – 172, January 2, 2013.

  • T. Subbulakshmi, S. M. Shalinie, and A. Ramamoorthi, “Detection and Classification of DDoS Attacks using Machine Learning Algorithms”, European Journal of Scientific Research, ISSN 1450-216X, Volume 47, No. 3, pp. 334 – 346, 2010.

  • H. Sedjelmaci, and M. Feham, “Novel Hybrid Intrusion Detection System for Clustered Wireless Sensor Network”, International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.4, July 2011.

  • T. H. Hai, E. N. Huh and M. Jo, “A Lightweight Intrusion Detection Framework for Wireless Sensor Networks”, Wireless Communications and mobile computing, Vol.10, Issue 4, pp. 559-572, 2010.

  • K. Revett et al., “A machine learning approach to keystroke dynamics based user authentication”, International Journal of Electronic Security and Digital Forensics, Vol. 1, No. 1, 2007.

  • G. Shu and D. Lee, “Testing Security Properties of Protocol Implementations – a Machine Learning Based Approach”, in Proceedings of 27th International Conference on Distributed Computing Systems (ICDCS'07), 2007.

  • D. Dolev and A. Yao, “On the security of public-key protocols”, IEEE Transaction on Information Theory 29, pages 198-208, 1983.

  • D. Angulin, “Learning regular sets from queries and counterexamples”, Information and Computation, 75, pp. 87-106, 1987.

  • K. Chellapilla and P. Y. Simard, “Using Machine Learning to Break Visual Human Interaction Proofs (HIPs)”, in Advances in Neural Information Processing Systems 17, pp. 265-272, 2005.

  • Simard PY, Steinkraus D, and Platt J, (2003) “Best Practice for Convolutional Neural Networks Applied to Visual Document Analysis,” in International Conference on Document Analysis and Recognition(ICDAR), pp. 958-962, IEEE Computer Society, Los Alamitos.

  • W. Yu and J. Cao, “Cryptography based on delayed chaotic neural networks”, Physics Letters A, Vol. 356, Issues 4–5, pp. 333-338, ISSN 0375-9601, August 14, 2006.

  • J. Yang et al., “Cryptanalysis of a cryptographic scheme based on delayed chaotic neural networks”, Chaos, Solitons & Fractals, Vol. 40, Issue 2, pp. 821-825, ISSN 0960-0779, April 30, 2009.

  • W. Kinzel and I. Kanter, “Neural Cryptography”, in Proceedings of the 9th International Conference on Neural Information Processing, Vol. 3, pp. 1351-1354, November 18-22, 2002.

  • M. Barreno et al., “The security of machine learning”, Journal Machine Learning, Vol. 81, Issue 2, pp. 121-148, November 2010.

  • Knowledge Discovery and Data Mining group, “KDD cup 1999”. [Online].Available:http://www.kdd.org/kddcup/index.php. [Accessed: March 3, 2013].

  • SpamBayes Project Group, “SpamBayes”. [Online]. Available: http://spambayes.sourceforge.net/. [Accessed: February 15, 2013].

  • V. Ford and A. Siraj, “Clustering of smart meter data for disaggregation”, in Proceedings of IEEE Global Conference on Signal and Information Processing, December, 2013.


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  • Applications of Machine Learning in Cyber Security

Abstract Views: 292  |  PDF Views: 3

Authors

B. N. Deepak
Department of Computer Science, RVCE, Bengaluru, India
H. Pavithra
Department of Computer Science, RVCE, Bengaluru, India

Abstract


Machine learning techniques have been applied in many areas of science due to their unique properties like adaptability, scalability, and potential to rapidly adjust to new and unknown challenges. Cyber security is a fast-growing field demanding a great deal of attention because of remarkable progresses in social networks, cloud and web technologies, online banking, mobile environment, smart grid, etc. Diverse machine learning methods have been successfully deployed to address such wide-ranging problems in computer security. This paper discusses and highlights different applications of machine learning in cyber security. This study covers phishing detection, network intrusion detection, testing security properties of protocols, authentication with keystroke dynamics, cryptography, human interaction proofs, spam detection in social network, smart meter energy consumption profiling, and issues in security of machine learning techniques itself.


Keywords


Security, Machine Learning, Survey.

References