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Mariia Rybalka

Publications and source records attributed to Mariia Rybalka.

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Do you dare to try Test-Driven Forensics? Increasing Trust in Desktop Forensics with ADARE

Digital forensic relies on validated tools and established procedures, yet the underlying operating systems, applications, and analysis tools evolve rapidly. This evolution can cause artifact behavior and tool outputs to drift, silently degrading repeatability and confidence in long-lived forensic interpretations. We present test-driven forensics, a practical approach that treats forensic expectations as executable specifications: expected artifacts and expected tool outputs are encoded as tests that can be rerun across versions to detect regressions. Crucially, our approach also enables State Transition Testing, validating the system's expected state after each user action rather than only performing post-mortem checks on a final disk image; this supports causal attribution and makes transient behavior testable. We implement the methodology in ADARE, an open-source framework that runs controlled experiments in virtual machines and simulates realistic user activity via computer-vision-guided GUI automation. ADARE includes a companion web platform for sharing experiments, environments, and results to facilitate independent reruns and peer verification. We evaluate ADARE in five case studies spanning artifact research and tool validation. In particular, a 25-version regression study of Autopsy reveals substantial, largely undocumented changes in exported report outputs, demonstrating how executable tests make drift measurable and reproducible at scale.

cs.CR

dewolf: Improving Decompilation by leveraging User Surveys

Analyzing third-party software such as malware or firmware is a crucial task for security analysts. Although various approaches for automatic analysis exist and are the subject of ongoing research, analysts often have to resort to manual static analysis to get a deep understanding of a given binary sample. Since the source code of encountered samples is rarely available, analysts regularly employ decompilers for easier and faster comprehension than analyzing a binary's disassembly. In this paper, we introduce our decompilation approach dewolf. We developed a variety of improvements over the previous academic state-of-the-art decompiler and some novel algorithms to enhance readability and comprehension, focusing on manual analysis. To evaluate our approach and to obtain a better insight into the analysts' needs, we conducted three user surveys. The results indicate that dewolf is suitable for malware comprehension and that its output quality noticeably exceeds Ghidra and Hex-Rays in certain aspects. Furthermore, our results imply that decompilers aiming at manual analysis should be highly configurable to respect individual user preferences. Additionally, future decompilers should not necessarily follow the unwritten rule to stick to the code-structure dictated by the assembly in order to produce readable output. In fact, the few cases where dewolf already cracks this rule lead to its results considerably exceeding other decompilers. We publish a prototype implementation of dewolf and all survey results on GitHub.

cs.CR