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Detect insider attacks using CNN in Decentralized Optimization

Citation Author(s):
Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li
Submitted by:
Gangqiang LI
Last updated:
14 May 2020 - 8:45am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Gangqiang Li
Paper Code:
SS-L12.6

Abstract 

Abstract: 

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers. Compared to the previous work, CNN can learn appropriate decision functions from the original state information without preprocessing through artificially designed rules, thereby alleviating the dependence on complex pre-designed models. Simulation results demon-strate that the proposed CNN-based approach can effectively improve the performance of detecting and localizing malicious agents, as compared with the conventional pre-designed score-based model.

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Detect insider attacks using CNN in Decentralized Optimization

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