An experience report on extracting and viewing memory events via wireshark
Bibliographic record
Abstract
Modern program analysis environments lack a principled method of monitoring low-level memory events. Such monitoring is of great value to activities like debugging, reverse engineering, vulnerability analysis, and security policy enforcement. Although current systems can be coerced to produce streams of memory events, most such techniques are inefficient or overly invasive and offer an unconstrained control over memory, which can subvert the reliability of such memory interposition as part of the attack engineering workflow. Our system, Cage, is a kernel-level mechanism for monitoring the memory events of a process. Like several existing memory trapping systems, Cage modifies and uses the functionality of the Linux kernel memory page subsystem. Cage translates the memory activity of a process into a packet-like format, and these events are exported over a device. The memory event packets can be captured and displayed using an existing analyzer (Wireshark). At present, Cage can monitor the memory events for the data, stack, and heap of a process as well as arbitrarily cage any other memory region. We have caged a Gnome login session successfully and noticed no ill effects. We discuss several potential applications that arise from imposing this network packet metaphor on memory events.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".