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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Hao Zhang, Yongdan Li, Zhihan Lv, Arun Kumar Sangaiah and Tao Huang, "A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 790-799, May 2020. doi: 10.1109/JAS.2020.1003099
Citation: Hao Zhang, Yongdan Li, Zhihan Lv, Arun Kumar Sangaiah and Tao Huang, "A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 790-799, May 2020. doi: 10.1109/JAS.2020.1003099

A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

doi: 10.1109/JAS.2020.1003099
Funds:  This work was supported by the National Key Research and Development Program of China (2017YFB1401300, 2017YFB1401304), the National Natural Science Foundation of China (61702211, L1724007, 61902203), Hubei Provincial Science and Technology Program of China (2017AKA191), the Self-Determined Research Funds of Central China Normal University (CCNU) from the Colleges’ Basic Research (CCNU17QD00 04, CCNU17GF0002), the Natural Science Foundation of Shandong Province (ZR2017QF015), and the Key Research and Development Plan–Major Scientific and Technological Innovation Projects of Shandong Province (2019JZZY020101)
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  • In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine (DBN-SVM). Sliding window (SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented. Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method’s real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

     

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    Highlights

    • This method is based on NetFlow design and can capture the data flow in the network with high detection efficiency.
    • The method mines frequent patterns in data based on nested sliding windows (NSW) and a genetic algorithm. It then compares these patterns with a safe frequent pattern set and an attack frequent pattern set, determining whether they represent normal data, known attacks or unknown attacks, to detect network intrusion behaviors efficiently in real time.
    • For attack-type data, a classification algorithm based on the deep belief network and support vector machine (DBN-SVM) is applied to accurately classify the attack type. The combination of DBN and SVM effectively improved the classification accuracy.
    • Compared with the existing detection methods, the intrusion detection method proposed in this paper is found to have higher accuracy and detection efficiency. Therefore, it is suitable for the current high-capacity and high-speed network environment.

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