The Flow-Based Anomaly Intrusion Detection System using Neural Network


  • Abuadlla Yousef University of Belgrade, Belgrade
  • Zoran Jovanovic University of Belgrade, Belgrade


Intrusion Detection system, Neural Network, NetFlow, Multilayer Perceptron, Anomaly detection


Due to the rapid increase of internet users and computer networks these days, there is an increased need for effctive security monitoring systems, such as Network Intrusion Detection Systems. Many researchers concentrate their efforts on this area using different type of approaches to build reliable detection system to protect computer networks from attacks. Flow-based intrusion detection systems are one of these approaches that rely on aggregated traffic metrics. Their main advantages are host independence and usability on high speed networks. In this paper, Neural Network anomaly intrusion detection system based on flow data is proposed for detecting attacks in the network traffic. The experimental results demonstrate that the designed
models are promising in terms of accuracy and computational time, and low rates of false positive alarms.

Author Biographies

Abuadlla Yousef, University of Belgrade, Belgrade

Computer Engineering Department

Zoran Jovanovic, University of Belgrade, Belgrade

Computer Engineering Department


Internet2 NetFlow: Weekly Reports., April 2008.

P. Haag. Nfsen: Netflow sensor.

D. Plonka. Flowscan., April 2008.

B. Claise. Cisco Systems NetFlow Services Export Version 9. Request for Comments: 3954, October 2004. IETF., April 2008.

Cisco IOS NetFlow Configuration Guide., April 2008.

IP Flow Information Export Working Group, April 2008.

J., Muna. M. and Mehrotra M., "Intrusion Detection System : A design perspective", 2rd International Conference On Data Management, IMT Ghaziabad, India. 2009.

M. Panda, and M. Patra, “Building an efficient network intrusion detection model using Self

Organizing Maps", proceeding of world academy of science, engineering and technology, Vol. 38. 2009.

M. Khattab Ali, W. Venus, and M. Suleiman Al Rababaa, "The Affect of Fuzzification on Neural Networks Intrusion Detection System", IEEE computer society.2009.

T. Zhou and LI Yang, "The Research of Intrusion Detection Based on Genetic Neural Network", Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, IEEE.2008.

J. Shum and A. Heidar Malki, "Network Intrusion Detection System Using Neural Networks", Fourth International Conference on Natural Computation, IEEE computer society.2008.

D. Novikov, V. Roman Yampolskiy, and L. Reznik, "Anomaly Detection Based Intrusion Detection", IEEE computer society.2006.

I. Ahmad, S. Ullah Swati and S. Mohsin, "Intrusions Detection Mechanism by Resilient Back

Propagation (RPROP)", European Journal of Scientific Research ISSN 1450-216X Vol.17 No.4,


S. Mukkamala, H. Andrew Sung, and A. Abraham, "Intrusion detection using an ensemble of intelligent paradigms", Journal of Network and Computer Applications 28. pp167–182.2005.

S. Jimmy and A. Heidar, "Network Intrusion Detection System using Neural Networks", IEEE computer society.2008.

M. Vallipuram and B. Robert, "An Intelligent Intrusion Detection System based on Neural Network", IADIS International Conference Applied Computing.2004.

Muna Mhammad T. Jawhar,” Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network”, International Journal of Computer Science and Security, Volume (4).2009

Rodrigo Braga,” Lightweight DDoS Flooding Attack Detection Using NOX/OpenFlow”, 35th Annual IEEE Conference on Local Computer Networks LCN 2010, Denver, Colorado

M. Al-Subaie, "The power of sequential learning in anomaly intrusion detection", degree master thesis, Queen University, Canada.2006.

D. Novikov, V. Roman Yampolskiy, and L. Reznik, "Artificial Intelligence Approaches For Intrusion Detection", IEEE computer society.2006.

S. Lília de Sá, C. Adriana Ferrari dos Santos, S. Demisio da Silva, and A. Montes, "A Neural

Network Application for Attack Detection in Computer Networks", Instituto Nacional de Pesquisas Espaciais – INPE, BRAZIL.2004.

James Cannady, “Artificial neural networks for misuse detection,” Proceedings of the 1998 National Information Systems Security Conference (NISSC'98), Arlington, VA, 1998.

J. Ryan, M. Lin, and R. Miikkulainen, “Intrusion Detection with Neural Networks,” AI Approaches to Fraud Detection and Risk Management: Papers from the 1997 AAAI Workshop, Providence, RI, pp. 72-79, 1997.

Srinivas Mukkamala, “Intrusion detection using neural networks and support vector machine,” Proceedings of the 2002 IEEE International Honolulu, HI, 2002

Java neural network framework,

Dima Novikov, Roman V. Yampolskiy and Leon Reznik,2006, “ Anomaly Detection Based Intrusion Detection”,Proceedings of the Third International Conference onInformation Technology: New Generations, IEEE.


KDDCup1999 :

Additional Files



How to Cite

Yousef, A., & Jovanovic, Z. . (2020). The Flow-Based Anomaly Intrusion Detection System using Neural Network. International Journal on Information Technology and Computer Science, 4(2). Retrieved from



Research Articles