David Schmidt

Dipl.-Ing. / BSc

David Schmidt
  • PreDoc Researcher
Publications (created while at TU Wien)
    • IoTFlow: Inferring IoT Device Behavior at Scale through Static Mobile Companion App Analysis
      Schmidt, D., Tagliaro, C., Borgolte, K., & Lindorfer, M. (2023). IoTFlow: Inferring IoT Device Behavior at Scale through Static Mobile Companion App Analysis. In CCS ’23: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (pp. 681–695). Association for Computing Machinery.
      DOI: 10.1145/3576915.3623211 Metadata
      The number of “smart” devices, that is, devices making up the Internet of Things (IoT), is steadily growing. They suffer from vulnerabilities just as other software and hardware. Automated analysis techniques can detect and address weaknesses before attackers can misuse them. Applying existing techniques or developing new approaches that are sufficiently general is challenging though. Contrary to other platforms, the IoT ecosystem features various software and hardware architectures. We introduce IoTFlow, a new static analysis approach for IoT devices that leverages their mobile companion apps to address the diversity and scalability challenges. IoTFlow combines Value Set Analysis (VSA) with more general data-flow analysis to automatically reconstruct and derive how companion apps communicate with IoT devices and remote cloud-based backends, what data they receive or send, and with whom they share it. To foster future work and reproducibility, our IoTFlow implementation is open source. We analyze 9,889 manually verified companion apps with IoTFlow to understand and characterize the current state of security and privacy in the IoT ecosystem, which also demonstrates the utility of IoTFlow. We compare how these IoT apps differ from 947 popular general-purpose apps in their local network commu- nication, the protocols they use, and who they communicate with. Moreover, we investigate how the results of IoTFlow compare to dynamic analysis, with manual and automated interaction, of 13 IoT devices when paired and used with their companion apps. Overall, utilizing IoTFlow, we discover various IoT security and privacy issues, such as abandoned domains, hard-coded credentials, expired certificates, and sensitive personal information being shared.
    • Large-scale Static Analysis of PII Leakage in IoT Companion Apps
      Schmidt, D. (2021). Large-scale Static Analysis of PII Leakage in IoT Companion Apps [Diploma Thesis, Technische Universität Wien]. reposiTUm.
      DOI: 10.34726/hss.2021.86548 Metadata
      Security and privacy problems of smart devices are often reported in the news. One possibility to improve the current situation are large-scale analyzes. Researchers and manufacturers can use such analysis to detect weaknesses, report them, and fix them before they get misused. However, to be able to perform a large-scale analysis, two difficulties need to be overcome. First, the diversity regarding software and hardware of smart devices makes a general approach difficult. Second, analyzes are often associated with high costs if physical devices are needed for the selected approach. We developed a static analysis approach for Internet of Things (IoT) companion applications (apps) to circumvent those difficulties. Companion apps are mobile apps, allowing their users to interact with smart devices. We focused on two aspects of companion apps that distinguish them from other applications: the communication over the local network and the used protocols. For this thesis, we use two analysis techniques to collect further information about the devices: taint analysis and value set analysis. We have chosen the latter for reconstructing URLs called by the applications and thereby detecting local communication. In this thesis, we analyzed in total 124 companion apps with our approach. We show the information obtained by the reconstructed endpoints. Furthermore, we present the flows found in two companion apps in detail, which contain threats to user's security and privacy. Overall, we make one step towards large-scale analysis of personally identifiable information (PII) leakage in IoT companion apps.