A Vulnerability Assessment Approach For Internet Of Things Enabled Transportation Networks Subjected To CyberPhysical Attacks
Bibliographic record
Abstract
Transportation networks play a vital role in society's well-being.While in the past, transportation networks were considered fragile only against threats in physical space (e.g., natural hazards), this is no longer the case.Previous events (e.g., Denial of Services attack against the Swedish Transport Administration) have highlighted the susceptibility of transportation domain to cyber-attacks.The integration of Internet of Things based wireless sensor networks in the sensing layer of a critical transportation infrastructure, increase the vulnerability of transportation networks to cyber-physical attacks.Current vulnerability assessment studies that treat transportation networks in the form of a graph (i.e., nodes, edges), overlook the security issues.In this paper, a new vulnerability assessment approach for transportation network subjected to cyber-physical attack, is proposed.The novelty of the approach relies on the consideration of vulnerabilities states, both in physical and cyber space, using a Bayesian network attack graph.A new probability indicator, that considers different attacker characteristics (e.g., skills) and control barriers (e.g., cameras) is proposed to drive the assignment of probability scores to vulnerability states.Following the probability-based ranking table, we measure the vulnerability of transportation network as a drop of network efficiency, after the removal of the highest probability-based ranked nodes.A transportation network case study is used to demonstrate the application of the approach.Monte Carlo simulations are performed as a method to evaluate the results, that indicate that transportation networks are probabilistically more susceptible to cyber-physical attacks, when IoT enabled transportation infrastructure is based on deficient control barriers.The approach is of interest to stakeholders (i.e., operators, civil and security engineers) who attempt to incorporate the cyber domain in vulnerability assessment procedures of their system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".