An intrusion detection system for DoS attacks based on neural networks

Omran Ali    Bentaher; Atia M.   Albhbah

المؤلفون

Omran Ali Bentaher
Atia M. Albhbah

الكلمات المفتاحية:

Denial-of-Service attack، Feature selection، Intrusion Detection Systems، Neural Networks

الملخص

ntrusion detection systems (IDSs) have become an essential component of computer security to detect attacks that occur despite the best preventive measures. A problem with majority of current intrusion detection systems is their rule-based nature. In this paper, we propose an optimized neural network based IDS for detecting DoS attacks. The proposed system consists of Multiple Layered Perceptron (MLP) decision block and a feature reduction preprocessing subsystem. The system was optimized and tested on benchmark KDDCUP’99 dataset. Several experiments have been conducted to choose the important features from full set of 41, based on three factors: training time, testing time and detection accuracy. Final optimized MLP IDS provides superior accuracy of 98.5%, substantially better than other referential IDS systems published up to now.

المراجع

- R.K.C. Chang, “Defending against Flooding-Based Distributed denial-of-Service Attack: A Tutorial,” IEEE Communication Magazine, October 2002, 42-51.

- W.Y. Luo, “A Lightweight System of Detecting DoS/Probe Attacks Based on Packet Header”, National Taiwan University of Science and Technology.

- D. E. Denning, “An intrusion detection model,” IEEE Transactions on Software Engineering, vol. 13, no. 2, pp. 222–232, 1987.

- M. Shyu, S. Chen, K. Sarinnapakorn, and L. Chang, “A novel anomaly detection scheme based on principal component classifier,” Proceedings of Third IEEE International Conference on Data Mining (ICDM03), pp. 172–179, 2003. 5- KDD Cup 1999. Available on:

http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, Ocotber 2007.

- J. McHugh, “Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory,” ACM Transactions on Information and System Security, vol. 3, no. 4, pp. 262–294, 2000.

- Fox, Kevin L., Henning, Rhonda R., and Reed, Jonathan H. (1990). “A Neural Network Approach Towards Intrusion Detection”. In Proceedings of the 13th National Computer Security Conference.

- Uwe Aickelin, Julie Greensmith, Jamie Twycross, “Immune System Approaches to Intrusion Detection–A Review” Natural computing,

Springer Netherlands, Volume 6, Number 4 / December, 2007, pp

-466

- http://www.mathworks.com/matlabcentral. [Accessed: 15 Jan 2016].

- Martin Riedmiller, Rprop - Description and Implementation Details Technical Report, Institute fur Logik, University of Karlsruhe, January 1994.

- Vemuri, V. (ed.), Enhancing Computer Security with Smart Technology, CRC Press, 2005.

- Sumeet Dua, Xian Du, Data Mining and Machine Learning in Cybersecurity, CRC Press, 2011.

- Mehadu Masud, Latifur Khan and Bhavani Thuraisingham, Data Mining Tools for Malware Detection, CRC Press, 2012.