Visible to the public Reliability Analysis of Concurrent Data based on Botnet Modeling

TitleReliability Analysis of Concurrent Data based on Botnet Modeling
Publication TypeConference Paper
Year of Publication2020
AuthorsCui, L., Huang, D., Zheng, X.
Conference Name2020 Fourth International Conference on Inventive Systems and Control (ICISC)
Date PublishedJan. 2020
ISBN Number978-1-7281-2813-9
Keywordsbotnet modeling, botnets, clustering variance method, composability, computer network security, concurrent data, data complex structure analysis, detection methods, Entropy, high-privilege systems, invasive software, Metrics, multidimensional permutation entropy, network modelling, network traffic time series, pattern clustering, pubcrawl, reliability, reliability analysis, resilience, Resiliency, security of data, security risks, spam, telecommunication traffic, time series, unsolicited e-mail

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance.

Citation Keycui_reliability_2020