2022/02/07 by Bara’ Nazzal, Nazzal, Bara', Manar H. Alalfi +1
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #Digital and Cyber Forensics #FOS: Computer and information sciences #Security and Verification in Computing #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2202.02895
openalex publication_date 2022/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a fully automated static analysis approach and a tool, Taint-Things, for the identification of tainted flows in SmartThings IoT apps. Taint-Things accurately identifies all tainted flows reported by one of the state-of-the-art tools with at least 4 times improved performance. Our approach reports potential vulnerable tainted flows in a form of a concise security slice, where the relevant parts of the code are given with the lines affecting the sensitive information, which could provide security auditors with an effective and precise tool to pinpoint security issues in SmartThings apps under test. We also present and test ways to add precision to Taint-Things by adding extra sensitivities; we provide different approaches for flow, path and context sensitive analyses through modules that can be added to Taint-Things. We present experiments to evaluate Taint-Things by running it on a SmartThings app dataset as well as testing for precision and recall on a set generated by a mutation framework to see how much coverage is achieved without adding false positives. This shows an improvement in performance both in terms of speed up to 4 folds, as well as improving the precision avoiding false positives by providing a higher level of flow and path sensitivity analysis in comparison with one of state of the art tools.