Swarm Intelligence
Swarm Intelligence
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DOI：
10.1007/978-3-030-00533-7
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发表时间：
2018-06
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影响因子：
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通讯作者：
Marco Dorigo;M. Birattari;Christian Blum;Anders Lyhne Christensen;A. Reina;V. Trianni
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文献类型：
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作者：
Marco Dorigo;M. Birattari;Christian Blum;Anders Lyhne Christensen;A. Reina;V. Trianni
非集中式行为，例如那些使机器人系统瘫痪的行为，很容易受到来自内部或外部敌对来源的故意破坏。群体机器人环境中的威胁可以通过目标、行为、环境或通信操纵来执行。这方面的实验研究仍然很少。我们研究了通过主动修改授权参与者之间的数据来执行的攻击场景。我们制定了一个强大的概率自适应防御机制，其目的不是在识别恶意代理，但提供的手段，以尽量减少攻击的后果的群体。该机制依赖于代理的概率的动态修改，以根据新的矛盾或确证的传入数据来改变其当前信息。我们在模拟中研究了几个实验条件。结果表明，在群体中的对手的存在，阻碍达成共识的多数意见时，使用基线方法，但有几个条件，我们的自适应防御机制是非常有效的。
Non-centralised behaviour such as those that characterise swarm robotics systems are vulnerable to intentional disruptions from internal or external adversarial sources. Threats in the context of swarm robotics can be executed through goal, behaviour, environment or communication manipulation. Experimental studies in this area are still sparse. We study an attack scenario performed by actively modifying the data between authorised participants. We formulate a robust probabilistic adaptive defence mechanism which does not aim at identifying malicious agents, but to provide the swarm with the means to minimise the consequences of the attack. The mechanism relies on a dynamic modification of the probability of agents to change their current information in view of new contradictory or corroborating incoming data. We investigate several experimental conditions in simulation. The results show that the presence of adversaries in the swarm hinders reaching consensus to the majority opinion when using a baseline method, but that there are several conditions in which our adaptive defence mechanism is highly efficient.