The Web Hacking Database, for short, is a Web Application Security Project dedicated to maintaining a list of web applications related security incidents. The goal is to serve as a tool for raising awareness of the web application security problem and provide information for statistical analysis of web applications security incidents. However to understand the risk associated with web hacks, we need to fully understand the likelihood and the impact of the attacks, and not just the technical details. To overcome this we have develop this application . here we using human immune system. By using functionality of dentritic cell and danger theory

1. Iman Khalkhali, Reza Azmi, Mozhgan Azimpour-Kivi and Mohammad Khansari ," Host-based Web Anomaly Intrusion Detection System, an Artificial Immune System Approach"in IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, September 2011
2. F. Gu, U. Aickelin, and J. Greensmith, "An agent-based classification model," in 9th European Agent Systems Summer School (EASSS2007),2007.
3. P. Harmer, P. Williams, G. Gunsch, and G. Lamont, "An artificial immune system architecture for computer security applications," IEEE Transactions on Evolutionary Computations, vol. 65, no. 3, pp. 252–280, November 2002.
4. 1J. R. Al-Enezi, 1M. F. Abbod & 2S. Alsharhan," Artificial Immune Systems – Models, Algorithms And Applications,"Ijrras 3 (2) , May 2010.
5. J. Zhang and Y. Liang, "Integrating innate and adaptive immunity for worm detection," in Proceedings of the Second International Workshop on Knowledge Discovery and Data Mining, 2009, pp. 693–696.
6. Matzinger. P, (1994) "Tolerance, Danger and the Extended Family,"Annual Review in Immunology, vol. 12, 2004, pp. 991-1045.
7. J. GreensmithU, Aickelin, and S. Cayzer, "Detecting danger: The dendritic cell algorithm," Robust Intelligent Systems, vol. 12, pp. 89–112, 2008.
8. U. Aickelin, P. Bentley, S. Cayzer, and J. Kim, "Danger theory: The link between ais and ids," Lecture Notes in Computer Sciences, vol. 2787, pp. 144–165, 2003.
9. L. N. de Castro and J. Timmis,"Artificial Immune Systems:A Novel Paradigm to Pattern Recognition,"Computing Laboratory University of Kent at Canterbury.
10. Antara Malakar and Tejbanta Singh Chingtham,"An Artificial Immune System For Humancomputer Interaction Through Speech,"International Journal of Instrumentation and Control Systems (IJICS) Vol. 2, No. 3, July 2012.
11. Chung-Ming Ou,Yao-Tien Wang nad C. R. Ou,"Intrusion Detectuin System Adapted from Agent-bsed Artificial Immune System,"2011 IEEE
12. International Conference on Fuzzy Sstems June 2011 27-30,2011,Taipei,Taiwan.
13. F. Gu, U. Aickelin, and J. Greensmith, "An agent-based classification model," in 9th European Agent Systems Summer School (EASSS2007), 2007.
14. U. Aickelin, P. Bentley, S. Cayzer, and J. Kim, "Danger theory: The link between ais and ids," Lecture Notes in Computer Sciences, vol. 2787, pp. 144–165, 2003.
15. Kim, An Artificial Immune System Architecture for Computer Security Applications. New York: John Wiley and Sons, 1978.
16. H. Fu, X. Yuan, and N. Wang, "Multi-agents artificial immune system (maais) inspired by danger theory for anomaly detection," in 2007 International Conference on Computational Intelligence and Security Workshops, 2007, pp. 570–573.
17. S. Forrest, A. Hofmeyr, T. Somayaji, and Longstaff, "A sense of self for unix processes," in Proceedinges of the 1996 IEEE Symposium on Research in Security and Privacy, 1996, p. 120V128.
18. J. Greensmith, U. Aickelin, and G. Tedesco, "Information fusion for anomaly detection with the dendritic cell algorithm. " Information Fusion, vol. 11, no. 1, pp. 21–34,2010.

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