Reinforced Secure Gossiping Against DoS Attacks in Post-Disaster Scenarios

Abstract

During and after a disaster, the perceived quality of communication networks often becomes remarkably degraded with an increased ratio of packet losses due to physical damages of the networking equipment, disturbance to the radio frequency signals, continuous reconfiguration of the routing tables, or sudden spikes of the network traffic, e.g., caused by the increased user activity in a post-disaster period. Several techniques have been introduced so far (mainly using data retransmission mechanisms) to tolerate such circumstances. Among them, gossiping has been shown to be efficient in the recovery from message losses. However, a conventional gossiping scheme may exhibit security problems, which can be exploited for further attacks (such as Denial of Service - DoS attack). For instance, the flooding method used by the gossiping can be used to forward the traffic towards many vulnerable nodes to drain their resources and compromise them. Typically, protection against DoS attacks is realized by using cryptographic primitives. However, their scalability limits and costs make them improper for emergency communications after a disaster. In this article, we introduce an approach based on reinforcement learning and game theory to protect the gossiping scheme from DoS attacks without incurring the costs of cryptographic primitives. In our method, nodes properly select which requests to satisfy, which in turn helps other nodes to avoid receiving manipulated gossip messages from malicious and colluded nodes. Additionally, our method operates without exploiting any cryptographic primitives, which prevents excessive energy waste that is undesired in post-disaster resilient networking. Simulation experiments performed in OMNeT++ confirmed the advantages of our approach over the reference schemes in terms of reliability, security, overhead, latency, and power efficiency.

Keywords

• Communication system security
• disaster-resilience
• gossiping algorithm
• game theory
• reinforcement learning