Intelligent detection method for abnormal big data in heterogeneous networks based on Bayesian classification

Issue title: Big Data Networking-Challenges and Applications

Guest editors: Zhixiang Hou and Chong Shen

Article type: Research Article

Authors: Liu, Ruijing^a | Luo, Xiaoting^{b; *}

Affiliations: [a] Beijing ZhongChaoWeiYe Information Security Technology Co., Ltd, Beijing 100080, China | [b] Beijing Information Science and Technology University, Beijing 100192, China

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In order to overcome the existing abnormal big data intelligent detection method, the problem of low detection accuracy and poor convergence is not carried out without abnormal big data classification. A new Bayesian classification based heterogeneous network anomaly big data intelligent detection is proposed in this paper. method. Design an abnormal big data intelligent detection architecture, use TcpDump collection tool to collect and process heterogeneous network traffic data, and build the relationship between bottleneck traffic and abnormal big data based on the processed data, through Fourier transform The method obtains the data frequency information and uses the Bayesian network classification method to realize the intelligent detection of abnormal big data in heterogeneous networks. The experimental results show that compared with the traditional method, the proposed method greatly improves the detection accuracy, convergence and anti-interference, and fully demonstrates that the proposed method has better detection effect.

Keywords: Bayesian classification, heterogeneous network, abnormal big data, intelligent detection

DOI: 10.3233/WEB-200437

Journal: Web Intelligence, vol. 18, no. 2, pp. 155-165, 2020