Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations
Bibliographic record
Abstract
Abstract A datacentre stores information and manages data access in fast and reliable manner. Failure of datacentre operation is not an option and can be catastrophic. Internet of things (IoT) devices in datacentre can automate management tasks and reduce human intervention and error. IoT devices can be used to manage many datacentre routine tasks such as monitoring physical infrastructure, updating software and configuration, monitoring network traffic, and automating alerting reports to respective authorities. The physical and cyber security of the datacentre can be handled by IoT technology by intrusion detection methods. By 2025, more than 25 billion things will be connected to the internet network, therefore massive data will be generated by different heterogeneous sources, and powerful processing engines such as complex event processing (CEP) are needed to handle such a fast and continuous stream of big data. The integration of machine learning (ML) and deep learning (DL) can enhance CEP by introducing new features such as automated rule extraction and self-healing mechanism. This study aims to provide an overview of CEP, as well as its features and potential for integration with IoT applications and ML/DL techniques. We provide a review of recent research works to highlight the capability and applicability of CEP technology to monitor physical facilities and cyber security in detail. This review also highlights several issues and challenges, and provides suggestions for future research. The highlighted insights and recommendations in this paper could raise efforts toward the development of future datacentres based on CEP technology.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".