Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106274
Title: Research on detection and defense mechanisms of DoS attacks based on BP neural network and game theory
Authors: Gao, Lijun
Li, Yanting
Zhang, Lu
Lin, Feng
Ma, Maode
Keywords: DoS Attacks
Security
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Gao, L., Li, Y., Zhang, L., Lin, F., & Ma, M. (2019). Research on detection and defense mechanisms of DoS attacks based on BP neural network and game theory. IEEE Access, 7, 43018-43030. doi:10.1109/ACCESS.2019.2905812
Series/Report no.: IEEE Access
Abstract: DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In this paper, BP (back propagation) neural networks and game theory are introduced to design detection methods and defense mechanisms for the DoS attacks. The BP neural network DoS attacks detection model uses KDDCUP99 as the dataset and selects multiple feature vectors from the dataset that can efficiently identify DoS attacks by large-scale training, which improves the accuracy of detecting DoS attacks to 99.977%. Furthermore, we use game theory to perform secondary analysis on DoS attacks that are not recognized by the neural network model, so that the detection rate of Dos attacks increases from 99.97% to 99.998%. Finally, we propose a DoS attacks defense strategy based on game theory. The simulation results show that the proposed detection method and defense strategy are effective for DoS attacks.
URI: https://hdl.handle.net/10356/106274
http://hdl.handle.net/10220/48889
DOI: 10.1109/ACCESS.2019.2905812
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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