Neural network help to determine the network attack such as Denial of Service (DoS), User to Root (U2R), Root to Local (R2L) and Probing. These propose technique based on network forensics and forensics work on the basis of post event. In propose application the post event are log files for that kddcup 99 is used as a standard dataset. This dataset is used as a input to the neural network for detecting the network based attack. In this paper Backpropagation algorithm is used for training the feed forward neural network and also help to identify the evidences of data source as intermediate side and end side. This paper provide the information about how to training and testing is perform in the the neural network and error calculation.

1. Reyadh Shaker Naoum, Namh Abdula Abid and Zainab Namh Al-Sultani, "An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System", IJCSNS International Journal of Computer Science and Network Security, VOL. 12 No. 3, March 2012.
2. Yang Xiang,Ke Li, Wanlei Zhou, "Low-Rate Ddos Attacks Detection And Traceback By Using New Information Metrics", IEEE transactions on information forensics and security, vol. 6, no. 2, pp 426-437, june 2011.
3. Igino Corona, Davide Ariu And Giorgio Giacinto,"HMM-Web: A Framework For The Detect ion Of Attacks Against Web Applications", IEEE International conference on communication, pp1-6, 2009.
4. Ying Xuan, Incheol Shin, My T. Thai, "Detecting Application Denial-Of-Service Attacks: A Group-Testing-Based Approach",IEEE transactions on p arallel and distributed systems, vol. 21, no. 8, pp 2103-1216, august 2010.
5. IftikharAhmad, Azween B Abdullah, Abdullah S Alghamdi, "Remote to Local Attack Detection Using Supervised Neural Network",5th International Conference for Internet Technology and Secured Transactions, Nov 8-11, 2010,
6. Atul Kant Kaushik , R. C. Joshi, "Network Forensic System for ICMP Attacks", International Journal of Computer Applications (0975 – 8887) Volume 2 – No. 3, May 2010.
7. Aida O. Ali, Ahmed I. Saleh, Tamer R. Badawy, "Intelligent Adaptive Intrusion Detection Systems Using Neural Networks", International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS Vol: 10 No: 01.
8. Sindhu. K. K , Dr. B. B. Meshram, "A Digital Forensic Tool For Cyber Crime Data Mining", IRACST Engineering Science And Technology: An International Journal (ESTIJ), ISSN: 2250-3498, Vol. 2, No. 1, 2012.
9. Muna Mhammad T. Jawhar and Monica Mehrotra, "Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network", International Journal of Computer Science and Security, Volume (4): Issue (3) -2009.
10. Ondrej Linda, Todd Vollmer, Milos Manic,"Neural Network Based Intrusion Detection System for Critical Infrastructures", International Joint Conference on Neural Networks-2009.
11. Liang Xie, Sencun Zhu,"Message Drop p ing Attacks In Overlay Networks:Attack Detection And Attacker Identification", IEEE 2006.
12. JeffHeaton," Programming Neural Networks in Java", November 16, 2005.
13. Andreas Makridakis, Elias Athanasopoulos, Spiros Antonatos, Demetres Antoniades, Sotiris Ioannidis, And Evangelos P. Markatos, " Understanding The Behavior Of Malicious Applications In Social Networks", IEEE 2010.
14. Tich Phuoc Tran, Longbing Cao , Dat Tran , Cuong Duc Nguyen, "Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting", (IJCSIS) International Journal of Computer Science and Information Security,Vol. 6, No. 1, 2009.
15. Nidal Qwasmi, Fay y az Ahmed, Ramiro Liscano, "simulation of ddos attacks on p2p networks" , IEEE International conference on HPCC, pp 610-614, 2011.
16. Ram Prasad Viswanathan, Youssif Al-Nashif, Salim Hariri," Application Attack Detection System (AADS):An Anomaly Based Behavior Analysis Approach", IEEE 2011.

Network Forensics Attack Detection Backpropagation Algorithm Neural Network.