A System for Denial of Service Attack Detection Based On Multivariate Corelation Analysis
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Abstract
in computing world, a denial-of-service (DoS) or is an process to make a machine or network resource unavailable to its regular users.DoS attack minimizes the efficiency of the server, inorder to increase the efficiency of the server it is necessary to identify the dos attacks hence MULTIVARIATE CORRELATION ANALYSIS(MCA)is used, this approach employs triangle area for obtaining the correlation information between the ip address. Based on extracted data the denial of service-attack is discovered and the response to the particular user is blocked, this maximizes the efficiency. Our proposed system is examined using KDD Cup 99 data set, and the influence of data on the performance of the proposed system is examined.
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References
V. Paxson, “Bro: A System for Detecting Network Intruders in Realtime,” Computer Networks, vol. 31, pp. 2435-2463, 1999.
P. Garca-Teodoro, J. Daz-Verdejo, G. Maci-Fernndez, and E. Vzquez, “Anomaly-based Network Intrusion Detection: Techniques, Systems and Challenges,” Computers & Security, vol. 28, pp. 18-28, 2009.
D. E. Denning, “An Intrusion-detection Model,” IEEE Transactions on Software Engineering, pp. 222- 232, 1987.
J. Yu, H. Lee, M.-S. Kim, and D. Park, “Traffic flooding attack detection with SNMP MIB using SVM,” Computer Communications, vol. 31, no. 17, pp. 4212-4219, 2008.
G. Thatte, U. Mitra, and J. Heidemann, “Parametric Methods for Anomaly Detection in Aggregate Traffic,” Networking, IEEE/ACM Transactions on, vol. 19, no. 2, pp. 512-525, 2011.
S. Jin, D. S. Yeung, and X. Wang, “Network Intrusion Detection in Covariance Feature Space,” Pattern Recognition, vol. 40, pp. 2185- 2197, 2007.
Z. Tan, A. Jamdagni, X. He, P. Nanda, and R. P. Liu, “Denial of- Service Attack Detection Based on Multivariate Correlation Analysis,” Neural Information Processing, 2011, pp. 756-765.