| Title | Detect Insider Attacks Using CNN in Decentralized Optimization |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Gangqiang, Wu, Sissi Xiaoxiao, Zhang, Shengli, Li, Qiang |
| Conference Name | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Keywords | composability, convolutional neural network (CNN), Distributed optimization, gossip algorithms, insider attack, Metrics, Neural networks, Optimization, privacy, pubcrawl, resilience, Resiliency, security, Signal processing, Signal processing algorithms, signal processing security, simulation, speech processing |
| 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 demonstrate 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. |
| DOI | 10.1109/ICASSP40776.2020.9053030 |
| Citation Key | li_detect_2020 |
Detect Insider Attacks Using CNN in Decentralized Optimization