Title
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.
Year
DOI
Venue
2020
10.1016/j.robot.2020.103556
Robotics and Autonomous Systems
Keywords
DocType
Volume
Online learning,Prefix interception,Routing attacks,Secure route
Journal
131
ISSN
Citations 
PageRank 
0921-8890
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Meng Meng100.34
Ruijuan Zheng200.34
Ruxi Peng300.34
Junlong Zhu43714.28
Mingchuan Zhang500.34
Qingtao Wu67019.88