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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".