Triangle Range Map Based Attack Detection (Dos) in Multivariate Correlation Analysis and Track –Back Prevention Mechanism

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-25 Number-2
Year of Publication : 2015
Authors : Y.Satyavathi, P.Jayaprakash


Y.Satyavathi, P.Jayaprakash "Triangle Range Map Based Attack Detection (Dos) in Multivariate Correlation Analysis and Track –Back Prevention Mechanism". International Journal of Computer Trends and Technology (IJCTT) V25(2):96-100, July 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Denial of Service attacksis a discriminating danger to the Internet. It is extremely arduous to follow back the aggressors for the reason that of memory less element of the web directing instruments. Thus, there's no successful what more, conservative procedure to handle this issue is. In this task, follows back of the aggressors are proficiently recognized furthermore to shield the information from the assailants utilizing Multivariate Correlation Analysis (MCA) by gauge precise system activity portrayal. MCAbased DoS assault discovery framework utilizes the standard of abnormality based location in assault acknowledgment. This makes our determination fit for criminologist work eminent also, obscure DoS assaults viably by taking in the examples of honest to goodness system movement exclusively. Proposed framework utilize a novel follow back strategy for DoS assaults that is in view of MCA in the middle of typical and DoSassault activity, which is in a broad sense unique in relation to generally utilized parcel stamping strategies. This system is utilized to spot the assailants with productivity and backings an larger than usual quantifiability .Furthermore, a triangle-area based method is utilized to upgrade and to accelerate the procedure of MCA. This system is connected to blast the assailants in an exceedingly wide space of system that was a great deal of temperate and shield the data from the aggressors.

[1] Baras J.S., A. A. Cardenas, , and V. Ramezani, “Distributed change detection for worms, DoS and other network attacks,” The American Control Conference, Vol.2, pp. 1008-1013, 2004..
[2]Daz-Verdejo.J ,P. Garca-Teodoro, G. Maci-Fernndez, and E.Vzquez,“Anomaly-based Network Intrusion Detection: Techniques,Systems and Challenges,”Computers& Security, vol. 28,pp. 18-28, 2009.
[3] Denning D.E., “An Intrusion-detection Model,” IEEE Transactions on Software Engineering, pp. 222-232, 1987.
[4]. Traffic flooding attack detection with SNMP MIB using SVMqJaehak Yu, Hansung Lee, Myung-Sup Kim *, Daihee ParkDepartment of Computer and Information Science, Korea University, Yeongi-Gun, Republic of Korea
[5]. Parametric Methods for AnomalyDetection in Aggregate TrafficGautamThatte, Student Member, IEEE, UrbashiMitra, Fellow, IEEE, and John Heidemann, Senior Member, IEEE.
[6]. A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis Zhiyuan Tan, ArunaJamdagni, Xiangjian He‡, Senior Member, IEEE,Priyadarsi Nanda, Member, IEEE, and Ren Ping Liu, Member, IEEE.
[7] C. Yu, H. Kai, and K. Wei -Shinn, “Collaborative Detection of DDoS Attacks over Multiple Network Domains,” IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 12, pp. 1649-1662, Dec. 2007.
[8] S.T. Sarasamma, Q.A. Zhu, and J. Huff, “Hierarchical Kohonenen Net for Anomaly Detection in Network Security,” IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 35, no. 2,pp. 302-312, Apr. 2005.
[9] S. Yu, W. Zhou, W. Jia, S. Guo, Y. Xiang, and F. Tang,“DiscriminatingDDoS Attacks from Flash Crowds Using Flow Correlation Coefficient,” IEEE Trans. Parallel and Distributed Systems, vol. 23, no. 6, pp. 1073-1080, June 2012.
[10] S. Jin, D.S. Yeung, and X. Wang, “Network Intrusion Detection in Covariance Feature Space,” Pattern Recognition, vol. 40, pp. 2185-2197, 2007.
[11] C.F. Tsai and C.Y. Lin, “A Triangle Area Based Nearest Neighbors Approach to Intrusion Detection,” Pattern Recognition, vol. 43, pp. 222-229, 2010.
[12] A. Jamdagni, Z. Tan, X. He, P. Nanda, and R.P. Liu, “RePIDS: AMulti Tier Real-Time Payload- Based Intrusion Detection System,” Computer Networks, vol. 57, pp. 811-824, 2013.
[13] Z. Tan, A. Jamdagni, X. He, P. Nanda, and R.P. Liu, “Denial -ofService Attack Detection Based on Multivariate Correlation Analysis,” Proc. Conf. Neural Information Processing, pp. 756-765, 2011

Denial-of-Service attack,multivariate correlations, network traffic characterization, triangle area, trace back Scheme