Securing IOTA Blockchain Against Tangle Vulnerability by Using Large Deviation Theory

IOTA has emerged as a promising blockchain platform specially designed for the Internet of Things (IoT). Its distributed ledger, called tangle, adopts a directed acyclic graph (DAG) structure to achieve fast transaction confirmation and high scalability. While the tangle tremendously mitigates blockchain performance concerns relative to a traditional single chain, it simultaneously increases the potential risk of double-spending attacks. Utilizing constructing illegal tangle branches to substitute for legitimate ones, attackers inside IOTA can launch double-spending attacks and seriously compromise the tangle security. In this work, we take the first step toward investigating the problem of tangle vulnerability by leveraging the large deviation theory. The proposed scheme, called SecTangle, can assist IOTA in effectively reducing the tangle vulnerability to resist double-spending attacks. The core idea is to explore the security threshold defined and deduced to affect the robustness of the tangle by evaluating the probability of tangle vulnerability. By adjusting the critical factors of the security threshold, fake tangle branches can be found by IOTA efficiently to prevent double-spending attacks. Besides, we further devise a transaction recovery algorithm to recover time-sensitive legitimate transaction branches. This article validates that the proposed scheme is efficient with comprehensive theoretical analysis and simulation experiments.