Home > Archives > IJSRST173419
Detection of Masquerade Attack by Data Driven Semi Global Alignment Approach
Authors(3) :-Snehal G.Sarade, Gorakh R. Bankar, Yogeshwari B. Narsale
Masquerade attackers behave like a authorized user to utilize user requirements. The semi-global alignment algorithm (SGA) is one of the most optimize and unique techniques to find out these attack but it has not extend the correctness and executions required by large scope, multiuser systems. To increase all the accuracy and the execution of this algorithm, we recommend the Data-Driven Semi-Global Alignment, DDSGA approach. For security purpose, DDSGA improve the scoring systems by altering various alignment arguments for each user. like wise, it allow small replacement in user command series by assinging small suitable different in the low-level showing of the command to ability to perform a task . It seems to make appropriate changes in the client using technique by updating the pattern of the a user as per to its current using technique. To fix the runtime located, DDSGA to make as little the alignment context and parallelizes the search out and to update. After showing the DDSGA phases, we show the experimental outputs. This output is to represent that DDSGA get the high hit ratio of 88.4% with low wrong positive rate. It improves the hit ratio of advanced SGA and minimizes Maxion-Townsend cost. So, DDSGA results in improving all the hit ratio and false positive rates with a capable calculation context.
Snehal G.Sarade, Gorakh R. Bankar, Yogeshwari B. Narsale
Masquerade attack, sequence alignment,mismatch alignment, security, intrusion attack
- T., S. E. Coulla and B. K. Szymanski, “Sequence alignment for masquerade detection,” J. Comput. Statist. Data Anal., vol. 52, no. 8, pp. 4116?4131, Apr. 2008.
- T. Lane and C. E. Brodley, “An application of machine learning to anomaly detection,” in Proc. 20th Nat. Inf. Syst. Security Conf., 1997, pp. 366?380.
- B. Christopher, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowl. Discovery, vol. 2, no. 2, pp. 121?167, 1998.
- Hisham. A. Kholidy and Fabrizio Baiardi, “CIDD: A cloud intrusion detection data set for cloud computing and masquerade attacks,” in Proc. 9th Int. Conf. Inf. Technol.: New Generations, Las Vegas, NV, USA, Apr. 2012, pp. 16?18.
- S. Malek and S. Salvatore, “Detecting masqueraders: A comparison of one-class bag-of-words user behavior modeling techniques,” in Proc. 2nd Int. Workshop Managing Insider SecurityThreats, Morioka, Iwate, Japan. Jun. 2010, pp. 3?13.
- B. Szymanski and Y. Zhang, “Recursive data mining for masquerade detection and author identification,” in Proc. IEEE 5th Syst., Man .Cybern. Inf. Assurance Workshop, West Point, NY, USA, Jun.2004, pp. 424?431.
- Subrat Kumar Dash, K. S. Reddy, and K. A. Pujari, “Adaptive Naive Bayes method for masquerade detection”, Security Commun.Netw., vol. 4, no. 4, pp. 410?417, 2011.
- A. Sharma and K. K. Paliwal, “Detecting masquerades using combination of Na??ve Bayes and weighted RBF approach,” J. Comput.Virology, vol. 3, no. 3, pp, 237?245, 2007.
Published in : Volume 3 | Issue 4 | May-June 2017
Date of Publication : 2017-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 134-140
Manuscript Number : IJSRST173419
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Snehal G.Sarade, Gorakh R. Bankar, Yogeshwari B. Narsale, "Detection of Masquerade Attack by Data Driven Semi Global Alignment Approach", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 4, pp.134-140, May-June-2017
URL : http://ijsrst.com/IJSRST173419