Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Identifying Masquerade Attackers Using The Improved DDSGA Approach

Author : M.Leela Sathwik 1 V.Saritha 2

Date of Publication :23rd November 2017

Abstract: I.T industry today is facing a major risk from countless attackers. Few attacks are easily identified and resolved but the majority of the attacks are difficult to identify. The main motive of the attackers is to theft the documents & demand money from the companies. There are various algorithms & approaches to identify the attackers but these algorithms & approaches have there owned advantages & disadvantages. The drawback of most of the algorithms is that they cannot provide high security & they take much time to execute. In this above paper, we have advised an IDDSGA algorithm which is based on SGA. we have put our efforts to improve the computational speed & the security of the semi-global alignment

Reference :

    1. M. Schonlau, W. DuMouchel, W. Ju, A. F. Karr, M. Theus, and Y.Vardi, “Computer intrusion: Detecting masquerades,” Statist. Sci.vol. 16, no. 1, pp. 58–74, 2001.
    2. S. E. Coull, J. W. Branch, B. K. Szymanski, and E. A. Breimer, “Intrusion detection: A bioinformatics approach,” in Proc. 19th Annu. Comput. Security Appl. Conf., Las Vegas, NV, USA, Dec.2003, pp. 24–33
    3. Scott E. Coull, Boleslaw K. Szymanski, “Sequence alignment for masquerade detection” Computational Statistics and Data Analysis 52 (2008) 4116–4131
    4. Hisham A. Kholidy, Fabrizio Baiardi, and Salim Hariri DDSGA: data-driven semi-global alignment approach for detecting masquerade attack.

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