Protecting xen hypercalls : intrusion detection/ prevention in a virtualization environment
Bibliographic record
Abstract
During the past few years virtualization has strongly reemerged from the shadow of the mainframe generation as a promising technology for the new generation of computers. Both the research and industry communities have recently looked at virtualization as a solution for security and reliability. With the increased usage and dependence on this technology, security issues of virtualization are becoming more and more relevant. This thesis looks at the challenge of securing Xen, a popular open source virtualization technology. We analyze security properties of the Xen architecture, propose and implement different security schemes including authenticated hypercalls, hypercall access table and hypercall stack trace verification to secure Xen hypercalls (which are analogous to system calls in the OS world). The security analysis shows that hypercall attacks could be a real threat to the Xen virtualization architecture (i.e., hypercalls could be exploited to inject malicious code into the virtual machine monitor (VMM) by a compromised guest OS), and effective hypercall protection measures can prevent this threat. The initial performance analysis shows that our security measures are efficient in terms of execution time and space.
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 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".