An examination of the security implications of the supervisory control and data acquisition (SCADA) system in a mobile networked environment: An augmented vulnerability tree approach.
Bibliographic record
Abstract
The increasing demand of supervisory control systems connected remotely to critical\ninfrastructure and recently the internet, has profiled a high threat level to the security and function of\ncontrol system; more especially Supervisory Control and Data Acquisition (SCADA) systems. It is\nvery crucial that operators and management are knowledgeable about these threats and get familiarize\nwith ways to trace and track information required mitigating the threats. In present day very\ncompetitive markets and with high levels of infrastructural investments, it is of vital importance for\ncorporations to be up-to-date with their SCADA networks so as to meet the challenges faced by\nremote and/or mobile access, use and cyber threat posed to the critical systems infrastructures. We\nhave examined the security implications of having the SCADA system exposed to the mobile and/or\ninternet environment. The paper deals with the following issues: Section one looks at types of\ninfrastructures and the general functionalities of SCADA and assesses the risks in mobile\nenvironment. The second section employs some of the recent methodologies applied to the SCADA\nsystem. Section three reports on some findings from analysis and critically evaluates the risks posed\nto the system using an augmented vulnerability tree approach. The last section draws from the\nfindings to re-evaluate, conclude and proposes some solutions on the risk issues of operating SCADA\nin a mobile networked environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".