An efficient algorithm for malicious update detection & recovery in distance vector protocols

The Internet infrastructure security has been gaining importance in recent years due to growing concerns for cyberwarfare. Among different network threats, the routing table poisoning attack is the most devastating and least researched topic, which needs immediate research attention. In this paper, we develop a scalable algorithm for detecting and recovering from router attacks in distance vector routing protocols. The algorithm is able to detect and recover from malicious updates under certain well-defined conditions. We carry out extensive simulation studies to evaluate the proposed pivot based algorithm for inconsistency recovery (PAIR) for three performance metrics, viz. detection probability, recovery probability and malicious distance under different network and attack scenarios. Our studies show that the PAIR is extremely scalable and offers high detection and recovery capability.