Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review
Bibliographic record
Abstract
Cloud computing, integral for data storage and online services, presents significant advantages over traditional data storage and distribution methods, including enhanced convenience, on-demand storage, scalability, and cost efficiency.Its growing adoption in securing Internet of Things (IoT) and cyber-physical systems (CPS) against various cyber threats offers numerous opportunities.Despite the continuous evolution of malware and the lack of a universally effective detection method, cloud environments provide a promising approach for malware detection.Cloud computing, recognized for its efficiency, scalability, flexibility, and reliability on elastic resources, is widely utilized in the IT industry to support IT infrastructure and services.However, one of the foremost security challenges faced is malware attacks.Certain antivirus scanners struggle to detect metamorphic or encrypted malware in cloud environments due to complexity and scale, allowing such threats to evade detection.High detection rates with precision in reducing false positives are essential.Machine learning (ML) classifiers, a vital component in Artificial Intelligence (AI) systems, require training on extensive data volumes to develop credible models with high detection rates.Traditional detection methods face challenges in identifying complex malware, as modern malware employs contemporary packaging and obfuscation techniques to circumvent security measures.This paper provides a detailed discussion on detecting malware in cloud environments and the advantages of cloud computing in safeguarding IoT and CPS from cyber attacks.It presents a survey on malware analysis and detection models, aiding researchers in identifying limitations of traditional malware detection models in cloud environments and inspiring the design of innovative models with enhanced quality of service levels.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".