A Classification of Intrusion Detection Systems in the Cloud
Bibliographic record
Abstract
Security is one of the most prominent challenges that hinder the acceleration of cloud adoption. Intrusion detection systems (IDSs) can be used to increase the security level of cloud environments. Therefore, the effectiveness of the IDS is a crucial issue for cloud security. However, the cloud presents new challenges and requirements, including scalability and adaptability, which effective IDSs need to address. Choosing the right deployment architecture significantly impacts the effectiveness of IDSs in the cloud. Additionally, robust IDSs need novel detection techniques to keep up with modern sophisticated attacks that target cloud environments. Hence, it is important to understand the advantages and limitations of different IDSs and how the deployment choice in cloud environments impacts the IDSs' effectiveness. This paper presents a novel classification scheme of the state-of-the-art of intrusion detection approaches in the cloud. This classification sheds light on the existing approaches with respect to the following aspects: deployment architecture and detection technique. We first classify the existing approaches based on their deployment architectures. Then, we present a comparative analysis of these approaches with respect to the detection techniques. We also provide detailed analysis of the strengths and weaknesses of existing approaches. The classification and analysis will help in the selection of the proper deployment architectures and detection techniques of IDSs in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".