A Cybersecurity Case for the Adoption of Blockchain in the Financial Industry
Bibliographic record
Abstract
The financial industry is faced with attractive business opportunities to adopt blockchain. To make such an investment, decision makers need answers to a number of questions including: what existing business problems will be solved, will blockchain solve them, and what long term benefits and new business opportunities blockchain can create. In this paper we analyze security aspects of these questions, focusing on the protection of integrity of data and financial transactions. We start from the analysis of an essential security architecture of financial systems which is based on the perimeter protection and traditional business process safeguards such as maker-checker. Subsequently, we look at the options on how to improve such an architecture to provide protection against malicious internal users and malware implanted inside the system; the vulnerabilities that have been exploited by organized criminal teams of attackers in the attacks seen lately. We show that the improvements based on the preventive safeguards, inherent to blockchain security architecture, provide strong protection against those attacks. Finally, we argue that in comparison with typically used detective measures (e.g. monitoring), security architecture based on the blockchain model provides superior protection against attacks using attack scenarios never seen before.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".