Bibliographic record
Abstract
In this study, the problem of malware proliferation is examined with emphasis on the role of artificial intelligence (AI) in its formation and propagation. The objective of this research is to analyze common malware attacks, their mechanisms, and prevention strategies, drawing upon literature. Methods involve a qualitative review of reported cases and cybersecurity guidelines published between 2010 and 2024. Findings indicate that AI both exacerbates malware threats through adversarial attacks, automated code generation, and phishing automation and offers tools for improved detection and defense. AI-driven anomaly detection, machine learning based intrusion prevention, and adaptive defense systems show promise in mitigating advanced threats. The review also highlights gaps in governance, adversarial machine learning defenses, and protection for IoT and embedded systems. It concludes that addressing malware proliferation requires coherent frameworks, administrative controls, and AI oversight. Future research should prioritize zero-trust architectures, adversarial machine learning defense strategies, supply chain resilience, and governance policies to ensure sustainable and adaptive cybersecurity defenses.
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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.025 |
| Open science | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".