DReAM: Deep Recursive Attentive Model for Anomaly Detection in Kernel Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
System logs and traces contain information that reflects the state of the system and serves as a rich source of knowledge for system monitoring from the application to the kernel layer. Moreover, logging of traces as a tool for monitoring the operation of a cyber-physical system is recommended by most safety standard organizations. However, because the data can be overwhelmingly huge within a short space of time, the use of models that do not rely only on known signatures for online anomaly detection becomes difficult to use due to the challenge of processing such an enormous amount of data at runtime. Hence, most practitioners resort to the use of signature-based tools. In this paper, we introduce an anomaly detection model that uses intra-trace and inter-trace context vectors with long short-term memory networks to overcome the challenge of online anomaly detection in cyber-physical systems. We test the performance of the model with publicly available datasets that reflect the internal and external control flow of an embedded application and our model demonstrates both the effectiveness and robustness in detecting an anomalous sequence in a system call stream.
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.
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 | 0.002 |
| Open science | 0.001 | 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 it