René Helmke (Fraunhofer FKIE), Elmar Padilla (Fraunhofer FKIE, Germany), Nils Aschenbruck (University of Osnabrück)

Firmware corpora for vulnerability research should be textit{scientifically sound}. Yet, several practical challenges complicate the creation of sound corpora: Sample acquisition, e.g., is hard and one must overcome the barrier of proprietary or encrypted data. As image contents are unknown prior analysis, it is hard to select textit{high-quality} samples that can satisfy scientific demands.
Ideally, we help each other out by sharing data. But here, sharing is problematic due to copyright laws. Instead, papers must carefully document each step of corpus creation: If a step is unclear, replicability is jeopardized. This has cascading effects on result verifiability, representativeness, and, thus, soundness.

Despite all challenges, how can we maintain the soundness of firmware corpora? This paper thoroughly analyzes the problem space and investigates its impact on research: We distill practical binary analysis challenges that significantly influence corpus creation. We use these insights to derive guidelines that help researchers to nurture corpus replicability and representativeness. We apply them to 44 top tier papers and systematically analyze scientific corpus creation practices. Our comprehensive analysis confirms that there is currently no common ground in related work. It shows the added value of our guidelines, as they discover methodical issues in corpus creation and unveil miniscule step stones in documentation. These blur visions on representativeness, hinder replicability, and, thus, negatively impact the soundness of otherwise excellent work.

Finally, we show the feasibility of our guidelines and build a new corpus for large-scale analyses on Linux firmware: LFwC. We share rich meta data for good (and proven) replicability. We verify unpacking, deduplicate, identify contents, provide ground truth, and demonstrate LFwC's utility for research.

View More Papers

Non-intrusive and Unconstrained Keystroke Inference in VR Platforms via...

Tao Ni (City University of Hong Kong), Yuefeng Du (City University of Hong Kong), Qingchuan Zhao (City University of Hong Kong), Cong Wang (City University of Hong Kong)

Read More

Automated Mass Malware Factory: The Convergence of Piggybacking and...

Heng Li (Huazhong University of Science and Technology), Zhiyuan Yao (Huazhong University of Science and Technology), Bang Wu (Huazhong University of Science and Technology), Cuiying Gao (Huazhong University of Science and Technology), Teng Xu (Huazhong University of Science and Technology), Wei Yuan (Huazhong University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic University)

Read More

IoT Software Updates: User Perspectives in the Context of...

S. P. Veed, S. M. Daftary, B. Singh, M. Rudra, S. Berhe (University of the Pacific), M. Maynard (Data Independence LLC) F. Khomh (Polytechnique Montreal)

Read More

CASPR: Context-Aware Security Policy Recommendation

Lifang Xiao (Institute of Information Engineering, Chinese Academy of Sciences), Hanyu Wang (Institute of Information Engineering, Chinese Academy of Sciences), Aimin Yu (Institute of Information Engineering, Chinese Academy of Sciences), Lixin Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Dan Meng (Institute of Information Engineering, Chinese Academy of Sciences)

Read More