In this paper, we propose to cluster malware samples based on their TLSH similarity. We apply this approach to clustering IoT malware samples as IoT botnets built from malware infected IoT devices are becoming an important trend. We study the performance of two distance-based clustering algorithms, k-medoid and OPTICS, on a large corpus of IoT malware samples when they are used with the TLSH difference metric to measure distances between samples. Our results show that neither of the two algorithms have acceptable clustering performance. Hence, we propose a new clustering algorithm, which achieves a performance superior to both k-medoid and OPTICS.

A program exhibits trigger-based behavior if it performs undocumented, often malicious, functions when the environmental conditions and/or specific input values match some pre-specified criteria. Checking whether such hidden functions exist in the program is important for increasing trustworthiness of software. In this paper, we propose a framework to effectively detect trigger-based behavior at the source code level. Our approach is semi-automated: We use automated source code instrumentation and mixed concrete and symbolic execution to generate potentially suspicious test cases that may trigger hidden, potentially malicious functions. The test cases must be investigated by a human analyst manually to decide which of them are real triggers. While our approach is not fully automated, it greatly reduces manual work by allowing analysts to focus on a few test cases found by our automated tools.

Embedded systems are the driving force for technological development in many domains such as automotive, healthcare, and industrial control in the emerging post-PC era. As more and more computational and networked devices are integrated into all aspects of our lives in a pervasive and “invisible” way, security becomes critical for the dependability of all smart or intelligent systems built upon these embedded systems. In this paper, we conduct a systematic review of the existing threats and vulnerabilities in embedded systems based on public available data. Moreover, based on the information, we derive an attack taxonomy for embedded systems. We envision that the findings in this paper provide a valuable insight of the threat landscape facing embedded systems. The knowledge can be used for a better understanding and the identification of security risks in system analysis and design.