The Flow-Based Anomaly Intrusion Detection System using Neural Network

Authors

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

Keywords:

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

Abstract

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

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Additional Files

Published

2020-09-02

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 http://ijitcs.info/index.php/ijitcs/article/view/14

Issue

Section

Research Articles