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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".