Malware detection in the cloud under Ensemble Empirical Mode Decomposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it