2019 VOL.2 Apr No.2

• Title: Deep camouflage network intrusion signal detection based on chaotic synchronization
• Name: Donald Franklin
• Company: Utrecht University, Utrecht Holland
• Abstract:

  In order to accurately and effectively detect the network intrusion signal under deep camouflage, a detection method based on chaotic synchronization is proposed. Firstly, the Gaussian mixture model of network data is established. Combined with the expectation maximization algorithm for post-detection, the concept of chaotic synchronization is proposed to detect the intrusion signal. The simulation results show that the proposed method has better detection performance for intrusion signals. ROC curve drawn detecting chaotic synchronization detecting method and the conventional method ARMA, chaotic performance indicating the detection of the synchronization algorithm ARMA superior to the traditional detection methods. This method has a good application prospect in network intrusion signal detection.

• Keyword: chaos, network intrusion, signal detection, chaotic synchronization
• DOI: 10.12250/jpciams2019020122
• Citation form: Donald Franklin.Deep camouflage network intrusion signal detection based on chaotic synchronization[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 54-59.

[1] GU Jin-sheng, JIANG Ling-ge, HE Di. An Intrusion Detection Method Based on Chaotic Synchronization[J]. Journal of Shanghai JIAOTONG University, 2009, 43(12): 1874-1880.

[2] Hu Guangbo, Zhou Yong. Study on Virtual Simulation for Ship Wake Based on Vega Prime[J]. Ship Electronic Engineering, 2010, 30(6): 91-94.

[3] Zhang Yi, Zhou Bingying, Hu Guangbo. Hardware System Design of Underwater Motor Pump Faults Diagnose Detector[J].Computer & Digital Engineering, 2012, 40(11): 162-166.

[4] HU Guang-bo, LIANGHong, XUQian. Research on Chaotic Feature Extraction of Ship Radiated Noise[J]. Computer Simulation, 2011, 28(2): 22-24.

[5] R. Gharieb. Higher order statistics based IIR notch filtering scheme for enhancing sinusoids in coloured noise[C]. IEEE Proceedings-Vision, Image and Signal Processing, 2000, 147(2): 115-121.

[6] WANG Zai-rong, LIU Yi-He. Application Study on Network Security Based on Association Rule[J]. Computer Simulation, 2011, 28(11): 130-133.

HU Guangbo, HE Xibing, GAN Xinnian. arget Detection Algorithm Based on Fractional Fourier Transform Cumulants[J]. Torpedo Technology, 2011, 19(5): 344-348.