A mechanism for identifying the guilt agent in a network using vector quantization and skew Gaussian distribution

  • Authors

    • P Praveen Kumar
    • Y Srinivas
    • M Vamsi Krishna
    2018-02-05
    https://doi.org/10.14419/ijet.v7i1.7.10638
  • Vector Quantization, Data Leakage, Intruder, Attacker, Transmission, Statistical Models

  • Abstract

    The recent state of art technologies has facilitated towards the ease of transfer of information from source to destination in a most comfortable and optimized medium. The channel transmitting discrepancies are rotted out by the sophisticated mechanisms that are being used currently using fiber optical cables etc… However sophisticated transmission mechanisms are available, the data transmission is always under threat due to intruder’s hackers and guilt agents, who try to either steal the information, override the information or share the information to miscreants. Therefore techniques are to be developed to identify these guilt agents so that the data transmission can be transformed in a more secure way without any pitfalls. This paper addresses in this direction by proposing models based on vector quantization and statistical models.

  • References

    1. [1] Poomagal, Shanmugam, Palanisamy Visalakshi, and Thiagarajan Hamsapriya.

      [2] "A novel method for clustering tweets in Twitter." International Journal of Web Based Communities 11.2 (2015):170-187.http://dx.doi.org/10.1504/IJWBC.2015.068540

      [3] Sebastiani, Fabrizio. "Machine learning in automated text categorization." ACM computing surveys (CSUR) 34.1 (2002): 1-47.

      [4] Khorsheed, Mohammad S., and Abdulmohsen O. Al-Thubaity. "Comparative evaluation of text classification techniques using a large diverse Arabic dataset." Language resources and evaluation 47.2 (2013):513-538. http://dx.doi.org/10.1007/s10579-013-9221-8

      [5] Ataa Allah, Fadoua. "Information retrieval: applications to English and Arabic documents." (2008).

      [6] Ghwanmeh, Sameh H. "Applying Clustering of hierarchical K-means like Algorithm on Arabic Language." International Journal of Information Technology3.3 (2005).

      [7] Beil, Florian, Martin Ester, and Xiaowei Xu. "Frequent term-based text Clustering." Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002. http://dx.doi.org/10.1145/775047.775110

      [8] Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze.Introduction to information retrieval. Vol.1.Cambridge: Cambridge university press, 2008 http://dx.doi.org/10.1017/CBO9780511809071

      [9] Aljaber, B., Stokes, N., Bailey, J., & Pei, J. (2010). Document clustering of scientific texts using citation contexts. Information Retrieval, 13(2), 101-131. http://dx.doi.org/10.1007/s10791-009-9108-x

      [10] Andrews, Nicholas O., and Edward A. Fox. "Recent developments in document clustering." (2007).

      [11] Carpineto, Claudio, et al. "A survey of web clustering engines." ACM Computing Surveys (CSUR) 41.3 (2009): 17.

      [12] Mubarak, Hamdy, and Kareem Darwish. "Using Twitter to collect a Multi-dialectal corpus of Arabic." ANLP 2014 (2014): 1.http://dx.doi.org/10.3115/v1/w14-3601

      Moh‟d Mesleh, Abdelwadood. "Feature sub-set selection metrics for Arabic text classification." Pattern Recognition Letters 32.14 (2011):1922-1929. http://dx.doi.org/10.1016/j.patrec.2011.07.

  • Downloads

  • How to Cite

    Praveen Kumar, P., Srinivas, Y., & Vamsi Krishna, M. (2018). A mechanism for identifying the guilt agent in a network using vector quantization and skew Gaussian distribution. International Journal of Engineering & Technology, 7(1.7), 149-151. https://doi.org/10.14419/ijet.v7i1.7.10638

    Received date: 2018-03-26

    Accepted date: 2018-03-26

    Published date: 2018-02-05