2018 Vol.1 Apr No.2

• Title: Detection of Network Intrusion Feature in Deep Camouflage Based on Chaotic Synchronization
• Name: Carl Byron
• Company: Macquarie University ,Australia
• Abstract:

  For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

• Keyword: Chaos; Network intrusion; Signal detection; Chaotic synchronization;
• DOI: 10.12250/jpciams2018020115
• Citation form: Carl Byron.Detection of Network Intrusion Feature in Deep Camouflage Based on Chaotic Synchronization[J]. Computer Informatization and Mechanical System, 2018, vol. 1, pp. 24-30.

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