Enhancing Boofuzz Process Monitoring for Closed-Source SCADA System Fuzzing
Bibliographic record
Abstract
Past cyber-attacks have demonstrated that Industrial Control and SCADA Systems are high-value targets for modern threat actors. In order to defend these classes of systems, it is necessary to detect and eliminate any pre-existing vulnerabilities before they can be leveraged into zero-day exploits. Different methods exist to find exploitable vulnerabilities in the software that runs these systems, one of which is known as fuzzing – wherein a system under test is exposed to a variety of input streams while simultaneously observed for unexpected behaviours, exceptions, or crashes. The aim of this research is to extend the Boofuzz network protocol-based fuzzing framework in order to effectively monitor a closed-source SCADA HMI endpoint during fuzz testing. Effective monitoring in this context is defined as the automated detection of target crashes during fuzzing which are recorded with an exception description, reproducing steps, and call stack trace. This data minimizes the time required for vulnerabilities discovered during fuzzing to be reproduced, investigated, and rectified by the software vendor. In order to accomplish this aim, our SCADA HMI is first analyzed to identify the fuzzing target and its runtime behaviours. A protocol fuzzer is then custom built for it using Boofuzz, with the existing target process monitor class extended to introduce new log file and debugger-based monitors. These extensions are then tested through fuzz tests of the SCADA HMI, the results from which demonstrate that vulnerabilities can be both automatically detected and recorded with the sufficient level of detail to expedite rectification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".