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With more IoT devices entering the consumer market, it becomes imperative to detect their security vulnerabilities before an attacker does. Existing binary analysis based approaches only work on firmware, which is less accessible except for those equipped with special tools for extracting the code from the device. To address this challenge in IoT security analysis, we present in this paper a novel automatic fuzzing framework, called IOTFUZZER, which aims at finding memory corruption vulnerabilities in IoT devices without access to their firmware images. The key idea is based upon the observation that most IoT devices are controlled through their official mobile apps, and such an app often contains rich information about the protocol it uses to communicate with its device. Therefore, by identifying and reusing program-specific logic (e.g., encryption) to mutate the test case (particularly message fields), we are able to effectively probe IoT targets without relying on any knowledge about its protocol specifications. In our research, we implemented IOTFUZZER and evaluated 17 real-world IoT devices running on different protocols, and our approach successfully identified 15 memory corruption vulnerabilities (including 8 previously unknown ones).

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Program code is a precious asset to its owner. Due to the easyto-reverse nature of Java, code protection for Android apps is of particular importance. To this end, code obfuscation is widely utilized by both legitimate app developers and malware authors, which complicates the representation of source code or machine code in order to hinder the manual investigation and code analysis. Despite many previous studies focusing on the obfuscation techniques, however, our knowledge on how obfuscation is applied by realworld developers is still limited.In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple thirdparty markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction.

No abstract