Industrial logistics optimisation in transportation with fuzzing techniques and reverse engineering to secure proprietary traffic control systems
Bibliographic record
Abstract
Objectives: Integrating industrial proprietary protocols in logistics transportation optimisation presents significant security challenges for traffic control systems. This study addresses the limitations of conventional fuzz testing tools in generating effective test cases for proprietary protocols.Methods: We propose a black-box fuzzing methodology combined with reverse engineering to analyze proprietary protocols in logistics optimisation. The approach begins with field segmentation and applies a multiple sequence alignment algorithm to extract protocol structures from network traffic. Heuristic rules identify key fields, such as length indicators, function codes, and constants, enabling protocol format reconstruction. A protocol state machine is then built to guide the fuzzing process. Test cases are generated through mutation strategies aligned with reconstructed protocol specifications, ensuring seamless interaction with the target system. We designed and implemented the Inter-Component Protocol Paradigm (ICPP) fuzzing tool to validate this approach.Results: Experiments on traffic control protocols (Modbus/TCP, UMAS, S7comm) demonstrate that ICPP fuzz outperforms Netzob in protocol reverse engineering and generates 1.27 × more effective test cases than Boofuzz. It also detected three denial-of-service vulnerabilities in Modicon TM200/221 PLCs.Conclusion: ICPP fuzz enhances fuzz testing efficiency by integrating protocol state machine guidance and reverse inference, significantly improving security in logistics transportation systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".