Safeguarding against prefix interception attacks via online learning

Abstract

In human–robot cooperation, the information interaction plays a key role. Most of the information interaction rely on Border Gateway Protocol (BGP), which is a vital route protocol on networks. However, the BGP is susceptible to the prefix interception attacks because the rightful origin of each prefix cannot be verified in BGP. For this reason, we propose a novel and effective route selection method against prefix interception attacks, which combines the resilience of routers and the historical performance of routers to choose a secure route. Moreover, we estimate the performance of BGP by introducing the definition of resilience and the historical performance of routers via online learning against the prefix interception attack. Furthermore, we analyze the bound of regret and obtain O(T) regret, where T denotes the time horizon. In addition, the proposed method is verified both on synthetic data and network simulations. The results show that the proposed method has more resilience against prefix interception attacks than Counter-Raptor.