Covert Channels Detection with Supported Vector Machine and Hyperbolic Hopfield Neural Network

  • Authors

    • G Yuvaraj
    • Siva Rama Lingham N
    • Rajkamal J
    2018-03-10
    https://doi.org/10.14419/ijet.v7i2.4.11166
  • Covert Channels, Support Vector Machine and Hyperbolic Hopfield Neural Network.

  • A mechanism that is intended to expose information against a security violation in a network is the use of network covert channel and it is difficult to detect information about data loss like location of loss using network covert channel. To identify the covert channel were the data pattern missing over the sharing of resources in networks. Several mechanisms are used to identify a large variation of covert channels. However, those mechanisms have more limitation like speed of detection, detection accuracy etc. In this paper, a new machine learning approaches called “Support Vector Machine and Hyperbolic Hopfield Neural Network†to overcome the drawbacks of existing methods. This approach is supported to classifying the different covert channels with data packets which is shared in networks and its supports to identifying the location of data loss or data pattern mismatched. Finally, the proposed methods properly detected covert channels with high accuracy and less detection high speed shared a network resources in effective manner.

     

     

  • References

    1. [1] Fahimeh Rezaei; Michael Hempel; Pradhumna Lal Shrestha; Sushanta Mohan Rakshit; Hamid Sharif “A novel Covert Timing Channel detection approach for online network traffic†IEEE Conference on Communications and Network Security (CNS) 2015.

      [2] R. Archibald, D. Ghosal, "A comparative analysis of detection metrics for covert timing channels", Journal of Computers & Security Elsevier, vol. 45, pp. 284-292, 2014.

      [3] R. A. Kemmerer, “A practical approach to identifying storage and timing channels,†in Proceedings of the 1982 IEEE Symposium on Security and Privacy, April 1982.

      [4] S. Gianvecchio, H. Wang, "An entropy-based approach to detecting covert timing channels", Dependable and Secure Computing IEEE Transactions on, vol. 8, no. 6, pp. 785-797, 2001.

      [5] Angelo Liguori; Francesco Benedetto; Gaetano Giunta; Nils Kopal; Arno Wacker†Analysis and monitoring of hidden TCP traffic based on an open-source covert timing channel†IEEE Conference on Communications and Network Security (CNS) 2015.

      [6] Hong Zhao; Yun Q. Shi †A phase-space reconstruction approach to detect covert channels in TCP/IP protocols †2010 IEEE International Workshop on Information Forensics and Security 2010.

      [7] S. Cabuk, “Network covert channels: Design, analysis, detection, and elimination,†Ph.D. dissertation, Purdue University, West Lafayette, IN., USA, December 2006.

      [8] F. Rczaei, M. Hempel, P. L. Shrestha, H. Sharif, "Evaluation and Analysis of Automated Covert Channel Modeling over Real Network Environment", IEEE Conference on Military Communication Conference (MILCOM), October 2014

      [9] Richard M. Stillman “Detecting IP covert timing channels by correlating packet timing with memory content†IEEE SoutheastCon 2008 .

      [10] Yusuf Ibrahim; Muhammed. B. Mu'Azu; Adewale. E. Adedokun; Yusuf. A. Sha'Aban “A performance analysis of logistic regression and support vector machine classifiers for spoof fingerprint detection†IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON) 2017

      [11] Steven Gianvecchio; Haining Wang†An Entropy-Based Approach to Detecting Covert Timing Channels†IEEE Transactions on Dependable and Secure Computing Year: 2011, Volume: 8, Issue: 6

      [12] S. Cabuk, “Network Covert Channels: Design, Analysis, Detection, and Elimination,†PhD dissertation, Purdue Univ., Dec. 2006.

      S. Cabuk, C. Brodley, and C. Shields, “IP Covert Timing Channels: Design and Detection,†Proc. ACM Conf. Computer and Comm. Security, Oct. 2004.

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  • How to Cite

    Yuvaraj, G., Rama Lingham N, S., & J, R. (2018). Covert Channels Detection with Supported Vector Machine and Hyperbolic Hopfield Neural Network. International Journal of Engineering & Technology, 7(2.4), 62-65. https://doi.org/10.14419/ijet.v7i2.4.11166