TRACE: Trusted Return-Path Authentication via Context and Lightweight Encryption for IoT Devices
Return-Oriented Programming (ROP) attacks pose a significant threat to the control-flow integrity of Internet of Things (IoT) devices, which operate in resource-constrained environments with limited memory isolation and runtime protection. Existing defenses, such as shadow stacks and message authentication code (MAC)-based schemes, face key limitations in IoT: shadow stacks depend on trusted hardware often absent in lightweight devices, while message authentication code (MAC) schemes lack semantic binding to the call path, making them vulnerable to replay attacks during recursion or stack reuse. To address these challenges, this paper proposes TRACE, a lightweight return-path authentication mechanism with path-semantic awareness, designed for IoT devices. TRACE dynamically encodes the function call context into an evolving path-state vector, which is then combined with the return address and cryptographically processed to generate a semantically unique authentication tag. At each function return, TRACE reconstructs the path state and verifies the tag to enforce precise runtime control-flow integrity. We evaluate TRACE in both synthetic and real-world attack scenarios. With the RIPE test suite, we demonstrate its robustness across five representative attack dimensions. Additionally, we identify a stack overflow vulnerability in the widely used libmodbus v2.9.3 protocol stack, construct a complete attack chain in a realistic IoT context, and validate TRACE’s effectiveness in mitigating such attacks. Experimental results show that TRACE reliably detects return-path tampering even without Address Space Layout Randomization (ASLR) or stack protections and incurs only a 5.3% runtime overhead, offering strong security with lightweight performance suitable for resource-constrained IoT deployments.
Authors
- Bing Gao
- Siqi Lu (ORCID: https://orcid.org/0000-0002-8593-9636)
- Rongkuan Ma (ORCID: https://orcid.org/0000-0002-4791-6847)
- Haijun Wang
- Yongjuan Wang
- Runze Zhao
- Yang Gao (ORCID: https://orcid.org/0000-0002-7755-4189)
- Yong Yu (ORCID: https://orcid.org/0000-0003-0667-077X)
Institutions
- PLA Information Engineering University (CN)
- Shaanxi Normal University (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1109/jiot.2025.3613486
- Primary Topic
- Smart Grid Security and Resilience
- Type
- article
- Field-Weighted Citation Impact
- 0.00