Malware detection in the cloud under Ensemble Empirical Mode Decomposition
Bibliographic record
Abstract
Cloud networks underpin most of todays' socio-economical Information Communication Technology (ICT) environments due to their intrinsic capabilities such as elasticity and service transparency. Undoubtedly, this increased dependence of numerous always-on services with the cloud is also subject to a number of security threats. An emerging critical aspect is related with the adequate identification and detection of malware. In the majority of cases, malware is the first building block for larger security threats such as distributed denial of service attacks (e.g. DDoS); thus its immediate detection is of crucial importance. In this paper we introduce a malware detection technique based on Ensemble Empirical Mode Decomposition (E-EMD) which is performed on the hypervisor level and jointly considers system and network information from every Virtual Machine (VM). Under two pragmatic cloud-specific scenarios instrumented in our controlled experimental testbed we show that our proposed technique can reach detection accuracy rates over 90% for a range of malware samples. In parallel we demonstrate the superiority of the introduced approach after comparison with a covariance-based anomaly detection technique that has been broadly used in previous studies. Consequently, we argue that our presented scheme provides a promising foundation towards the efficient detection of malware in modern virtualized cloud environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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; 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".