A research using hybrid RBF/Elman neural networks for intrusion detection system secure model

作者：Tong Xiaojun^*; Wang Zhu; Yu Haining

来源：Computer Physics Communications, 2009, 180(10): 1795-1801.

DOI：10.1016/j.cpc.2009.05.004

• Intrusion detection
• Hybrid RBF/Elman neural network
• Memory of events
• Anomaly detection
• Misuse detection

A hybrid RBF/Elman neural network model that can be employed for both anomaly detection and misuse detection is presented in this paper. The IDSs using the hybrid neural network can detect temporally dispersed and collaborative attacks effectively because of its memory of past events. The RBF network is employed as a real-time pattern classification and the Elman network is employed to restore the memory of past events. The IDSs using the hybrid neural network are evaluated against the intrusion detection evaluation data sponsored by U.S. Defense Advanced Research Projects Agency (DARPA). Experimental results are presented in ROC curves. Experiments show that the IDSs using this hybrid neural network improve the detection rate and decrease the false positive rate effectively.

• 出版日期2009-10
• 单位哈尔滨工业大学