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

有效地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展，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：如果使用的一个或多个平台受到损害，并将错误的值传播给其他服务器，则生成的答案将是不正确的。仅仅依靠身份验证并不总是能够防止此类攻击的发生。本项目研究并行算法中存在的漏洞，旨在了解如何检测功能失调的代理，隔离那些受损的代理并恢复系统功能。对去中心化计算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到狂热者在社会环境中的影响，他们注入虚假信息，将集体决策的结果导向有利于自己利益的特定行为。

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.