Security Challenges in IoT-Blockchain Integrated Ecosystems

Abstract

The convergence of Internet-of-Things (IoT) infrastructures with blockchain platforms promises verifiable data provenance, tamper-evident logging, and decentralized coordination across untrusted devices. Yet, the integration itself creates new security exposures at the seams between constrained edge devices, resource-heavy distributed ledgers, and the middleware that binds them. This manuscript analyzes the multi-layer attack surface of IoT–blockchain systems and demonstrates, via a simulation-driven study, how design choices—permissioning model, consensus algorithm, key management, smart-contract engineering, and off-chain/on-chain partitioning—affect risk. We first synthesize the dominant threats: physical compromise of endpoints; identity spoofing and Sybil amplification; side-channel leakage through traffic metadata; gateway bottlenecks susceptible to denial-of-service; oracle and cross-chain manipulation; smart-contract logic and reentrancy bugs; and privacy/regulatory conflicts tied to immutability. We then propose a methodology for evaluating security posture using a layered reference architecture and a logit-based statistical model that estimates the probability of successful attacks under different controls.

In a discrete-event simulation of 5,000 heterogeneous IoT nodes bridged to (a) a permissioned PBFT network and (b) a public PoA sidechain, we observe that enabling hardware roots-of-trust, edge-rate-limiting, and formally verified smart contracts reduces estimated attack success odds by 61–78% (scenario-dependent) while incurring modest latency overhead (<18% median) and marginal energy costs at the edge (<6%). The results emphasize that “blockchain” does not neutralize classical IoT threats; rather, it can amplify them if identity, oracles, and gateways are weak. We conclude with a prioritized control portfolio and engineering guidelines to harden real-world deployments without sacrificing the performance envelope needed for time-sensitive IoT workloads.