Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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Analyzing Effective of Activation Functions on Recurrent Neural Networks for Intrusion Detection

  • Le, Thi-Thu-Huong (School of Computer Science and Engineering, Pusan National University) ;
  • Kim, Jihyun (School of Computer Science and Engineering, Pusan National University) ;
  • Kim, Howon (School of Computer Science and Engineering, Pusan National University)
  • Received : 2016.07.02
  • Accepted : 2016.07.28
  • Published : 2016.09.30
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Abstract

Network security is an interesting area in Information Technology. It has an important role for the manager monitor and control operating of the network. There are many techniques to help us prevent anomaly or malicious activities such as firewall configuration etc. Intrusion Detection System (IDS) is one of effective method help us reduce the cost to build. The more attacks occur, the more necessary intrusion detection needs. IDS is a software or hardware systems, even though is a combination of them. Its major role is detecting malicious activity. In recently, there are many researchers proposed techniques or algorithms to build a tool in this field. In this paper, we improve the performance of IDS. We explore and analyze the impact of activation functions applying to recurrent neural network model. We use to KDD cup dataset for our experiment. By our experimental results, we verify that our new tool of IDS is really significant in this field.

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References

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