On Preventing and Mitigating Cache Based Side-Channel Attacks on AES System in Virtualized Environments
Bibliographic record
Abstract
Cloud computing aims to cut costs through a reduction in spending on equipment, infrastructure, and software by applying the multi-tenancy feature. Despite all the benefits of multi-tenancy, it is still a source of risk in cloud computing. Cloud adoption may be hampered by security concerns if suitable cloud-based security solutions are not available. Moreover, virtualization that enables multi-tenancy, considered one of the main components of a cloud, introduces major security risks and does not offer appropriate isolation between different instances running on the same physical machine. In this paper, we present a preliminary idea that may support the development of new countermeasures for a particular type of threat, namely cache-based side-channel attacks that target cache memories in virtualized environments. Attackers specifically target virtual machines in this type of attack to create many side channels and gather sensitive data. Additionally, this research offers preliminary concepts to aid in developing of solutions or defenses that enable us to identify unusual activity that could point to attacks associated with multi-tenancy, as well as security measures that preserve the benefits of multi-tenancy while lowering security concerns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".