Analysis of Symmetric, Asymmetric, and Hybrid Key Encryption Techniques in Cryptocurrency Ransomware Attacks
Bibliographic record
Abstract
Ransomware, a malicious software type leveraging cryptography, has evolved into a significant global cybersecurity threat. This paper explores the use of symmetric, asymmetric, and hybrid encryption techniques in ransomware attacks and its impacts. Symmetric cryptography is favored for its speed, while asymmetric systems provide enhanced key security. Hybrid systems, combining the strengths of both, offer the most effective encryption method for ransomware operations. Through analysis of notable ransomware like Jigsaw, Archiveus, and WannaCry, this paper highlights their cryptographic methods, impact, and operational strategies. The study underscores the importance of robust countermeasures, including updated cybersecurity protocols and awareness, to mitigate ransomware risks. Additionally, the paper evaluates the role of evolution in ransomware attacks, including the importance of cryptocurrencies enabling anonymous ransom payments. The findings emphasize the need for comprehensive strategies that incorporate both technological advancements and human-centered approaches to cybersecurity. By addressing these challenges, individuals and organizations can reduce the frequency and severity of ransomware attacks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".