Anomaly Detection Based on Access Behavior and Document Rank Algorithm

International Journal of Computer Trends and Technology (IJCTT)          
© - December Issue 2013 by IJCTT Journal
Volume-6 Issue-4                           
Year of Publication : 2013
Authors :Prajwal R Thakare , K. Hanumantha Rao


Prajwal R Thakare , K. Hanumantha Rao"Anomaly Detection Based on Access Behavior and Document Rank Algorithm"International Journal of Computer Trends and Technology (IJCTT),V6(4):230-235 December Issue 2013 .ISSN Published by Seventh Sense Research Group.

Abstract:- -Distributed denial of service (DDoS) attack is ongoing dangerous threat to the Internet. Commonly, DDoS attacks are carried out at the network layer, e.g., SYN flooding, ICMP flooding and UDP flooding, which are called DDoS attacks. The intention of these DDoS attacks is to utilize the network bandwidth and deny service to authorize users of the victim systems. Obtain from the low layers, new application-layer-based DDoS attacks utilizing authorize HTTP requests to overload victim resources are more undetectable. When these are taking place during crowd events of any popular website, this is the case is very serious. The state-of-art approaches cannot handle the situation where there is no considerable deviation between the normal and the attacker’s activity. The page rank and proximity graph representation of online web accesses takes much time in practice. There should be less computational complexity, than of proximity graph search. Hence proposing Web Access Table mechanism to hold the data such as ‘who accessed what and how many times, and their rank on average” to find the anomalous web access behavior. The system takes less computational complexity and may produce considerable time complexity.


[1] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,”ACM Computing Surveys, vol. 41, no. 3, pp. 15:1–15:58, 2009.
[2] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank citation ranking: Bringing order to the web,” Stanford InforLab, Tech. Rep. 1999-66, 1999.
[3] B. Sch¨olkopf, J. Platt, J. Shawe-Taylor, A. Smola, and R. Williamson, “Estimating the support of a high-dimensional distribution,” Neural computation, vol. 13, no. 7, pp. 1443– 1471, 2001.
[4] C. Scott and R. Nowak, “A Neyman-Pearson approach to statistical learning,” IEEE Transactions on Information Theory, vol. 51, no. 11,pp. 3806–3819, 2005.
[5]“Learning minimum volume sets,” Journal of Machine Learning Research, vol. 7, pp. 665–704, 2006.
[6] C. Scott and E. Kolaczyk, “Nonparametric assessment of contamination in multivariate data using generalized quantile sets and fdr,” Journal of Computational and Graphical Statistics, vol. 19, no. 2, pp. 439– 456,2010.
[7] A. Hero III, “Geometric entropy minimization (GEM) for anomaly detection and localization,” in Proc. Advances in Neural Information Processing Systems, vol. 19, Vancouver, BC, Canada, 2006, pp. 585–592.
[8] S. Byers and A. Raftery, “Nearest-neighbor clutter removal for estimating features in spatial point processes,” Journal of the American Statistical Association, vol. 93, no. 442, pp. 577–584, 1998.

Keywords:-Anomaly, DDos- Distributed denial of service, SYN-Flooding, HTTP Requests.