Countering Active Attacks on RAFT-Based IoT Blockchain Networks

Abstract

This paper considers an Internet of Things (IoT) blockchain wireless network consisting of a leader node and various follower nodes which together implement the RAFT consensus protocol to verify a blockchain transaction, as requested by a blockchain client. Further, two kinds of active attacks, i.e., jamming and impersonation, are considered on the IoT blockchain network due to the presence of multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active</i> malicious nodes in the close vicinity. When the IoT network is under a jamming attack, we utilize the stochastic geometry tool to derive the closed-form expressions for the coverage probabilities for both uplink and downlink IoT transmissions (which eventually translate to blockchain transaction success rate). On the other hand, when the IoT network is under an impersonation attack, we propose a novel method that enables a receive IoT node to exploit the pathloss of a transmit IoT node as its fingerprint to implement a binary hypothesis test for transmit node identification. To this end, we also provide the closed-form expressions for the probabilities of false alarm, missed detection, and miss-classification. Finally, we present detailed simulation results that indicate the following: i) the coverage probability (and hence blockchain transaction success rate) improves as the jammers’ locations move away from the IoT network, ii) the three error probabilities decrease (i.e., chances of corruption of the blockchain ledger data due to false data injection by malicious node decrease) as a function of the quality of the link between the transmit and receive IoT node.