Comparative Analysis of Malware Detection Approaches in Cloud Computing
Bibliographic record
Abstract
The widespread use of cloud computing techniques in many applications renders cloud computing environments extremely vulnerable to malware infections and novel attacks.The flexibility, scalability, and elasticity cloud computing provide add to the difficulty of detecting malicious software in cloud computing environments.In this study, we analyze malware attacks that infect cloud computing environments.Moreover, we elaborate on different malicious software detection approaches in cloud computing environments.Furthermore, we evaluate these approaches by considering other perspectives (i.e., malware detection accuracy and deployed analytical techniques).More than 50% of the approaches of the malware detection papers (in this survey) used deep learning techniques in cloud computing environments.In addition, the majority of authors preferred to use dynamic malware analysis.Deep learning and dynamic analysis are powerful, complementary approaches in malware detection.Dynamic analysis observes the runtime behavior of programs, such as API calls, file operations, and network activity, to detect malicious patterns in controlled environments like sandboxes.When integrated with deep learning, this behavioral data can be analyzed more effectively using advanced models like RNNs or CNNs.Deep learning enhances dynamic analysis by identifying complex, hidden patterns in malware behavior and adapting to zero-day threats.This combination provides a robust defense mechanism, particularly in cloud computing, where large-scale and real-time detection capabilities are critical.The rates of detection are vacillated from 79% to 99%.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".