Hybrid BP/CNN neural network for intrusion detection

  • Authors:

  • Yao Yu;Gao Fu-xiang;Yu Ge

  • Affiliations:

  • Northeastern University, Shenyang, Liaoing, China;Northeastern University, Shenyang, Liaoing, China;Northeastern University, Shenyang, Liaoing, China

  • Venue:
  • InfoSecu '04 Proceedings of the 3rd international conference on Information security
  • Year:
  • 2004

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Abstract

In order to improve the intrusion detection rates and reduce false positives, a hybrid BP/CNN neural network is constructed, which has both the capability of real-time classification which BP has and the functionality of time-delay, collection and judgment which chaotic neuron has. Because this intrusion detection approach has flexible time-delay characteristic, it corresponds to the requirement of intrusion detection nowadays. The simulation tests to FTP brute-force attacks are conducted using samples captured from data traffic in local computer network. The test results are drawn by ROC curves. The intrusion criterion with high rate intrusion detection and low rates of false alarms can be found. The intrusion detection approach in this paper may be generalized to other intrusion detection systems.