Using VMM-based sensors to monitor honeypots
Bibliographic record
Abstract
Virtual Machine Monitors (VMMs) are a common tool for implementing honeypots. In this paper we examine the implementation of a VMM-based intrusion detection and monitoring system for collecting information about attacks on honeypots. We document and evaluate three designs we have implemented on two open-source virtualization platforms: User-Mode Linux and Xen. Our results show that our designs give the monitor good visibility into the system and thus, a small number of monitoring sensors can detect a large number of intrusions. In a three month period, we were able to detect five different attacks, as well as collect and try 46 more exploits on our honeypots. All attacks were detected with only two monitoring sensors. We found that the performance overhead for monitoring such intrusions is independent of which events are being monitored, but depends entirely on the number of monitoring events and the underlying monitoring implementation. The performance overhead can be significantly improved by implementing the monitor directly in the privileged code of the VMM, though at the cost of increasing the size of the trusted computing base of the system.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".