CCF-BSF: CIF: Small: Identification and Isolation of Malicious Behavior in Multi-Agent Optimization Algorithms

CCF-BSF：CIF：小：多代理优化算法中恶意行为的识别和隔离

• 批准号: 1714672
• 负责人: Anna Scaglione
• 金额: $ 18万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714672&HistoricalAwards=false
• 关键词: CCF; BSF; CIF; Small; Identification

Harnessing effectively the power of cloud computing and the benefits of ubiquitous data collection requires parallel advances in parallel algorithms, also known as multi-agent optimization. Unfortunately, these methods are vulnerable to cyber-attacks: if one or more of the platforms used is compromised and spreads incorrect values to other servers, the answers produced will be incorrect. It is not always possible to prevent this kind of attacks from occurring by relying on authentication alone. This project studies the vulnerabilities that exist in parallel algorithms, with the intent of understanding how to detect dysfunctional agents, isolate those that are compromised and restore the system functionality. The study of vulnerabilities in decentralized computation has important ramifications that go beyond the engineering discipline. In social science multi-agent optimization is used as a model for social learning. The study of malicious behavior will capture the effect of zealots in social settings that inject false information, steering the outcome of collective decisions towards specific actions that favors their interests.

有效利用云计算的力量和无处不在的数据收集的好处需要并行算法的并行进步，也称为多代理优化。不幸的是，这些方法很容易受到网络攻击：如果所使用的一个或多个平台受到损害并将错误的值传播到其他服务器，则生成的答案将是错误的。仅依靠身份验证并不总是能够防止此类攻击的发生。该项目研究并行算法中存在的漏洞，目的是了解如何检测功能失调的代理、隔离受到损害的代理并恢复系统功能。对分散计算中的漏洞的研究具有超越工程学科的重要影响。在社会科学中，多智能体优化被用作社会学习的模型。对恶意行为的研究将捕捉社会环境中狂热分子注入虚假信息的影响，将集体决策的结果转向有利于他们利益的具体行动。

项目成果

Joint Network Topology and Dynamics Recovery From Perturbed Stationary Points

• DOI: 10.1109/tsp.2019.2929471
• 发表时间: 2019-07
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Hoi-To Wai;A. Scaglione;B. Barzel;Amir Leshem

Identifying Susceptible Agents in Time Varying Opinion Dynamics through Compressive Measurements

通过压缩测量识别时变意见动态中的易感代理

• 发表时间: 2018
• 期刊: ICASSP 2018
• 作者: Wai, Hoi To;Ozgladar, Asuman E.;Scaglione, Anna
• 通讯作者: Scaglione, Anna

Blind Community Detection From Low-Rank Excitations of a Graph Filter

• DOI: 10.1109/tsp.2019.2961296
• 发表时间: 2018-09
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Hoi-To Wai;Santiago Segarra;A. Ozdaglar;A. Scaglione;A. Jadbabaie

Community Inference from Graph Signals with Hidden Nodes

• DOI: 10.1109/icassp.2019.8683001
• 发表时间: 2019-05
• 期刊: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Hoi-To Wai;Yonina C. Eldar;A. Ozdaglar;A. Scaglione

Accelerating incremental gradient optimization with curvature information

• DOI: 10.1007/s10589-020-00183-1
• 发表时间: 2018-05
• 期刊: Computational Optimization and Applications
• 影响因子: 2.2
• 作者: Hoi-To Wai;Wei Shi;César A. Uribe;A. Nedić;A. Scaglione