A Framework for Anomaly Detection in Time-Driven and Event-Driven Processes using Kernel Traces
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
Model-checking and verification using Kripke structures and computational tree logic* (CTL*) use abstractions from the model/process/application to create the state-transition graphs that verify the model behavior. This scheme of profiling the performance of a process imports that the depth of the process operation correlates with the level abstraction. However, because of state explosion problems, these abstractions tend to restrict the scope to create manageable execution states. Therefore, for context modeling, this procedure does not generate a fine-grained behavioral model as generated states limit the ability of the abstraction to capture the execution time interactions amongst the processes, the hardware, and the kernel. Hence, in this paper, we present an end-to-end framework that comprises auto-encoders and probabilistic models to understand the behavior of system processes and detect deviant behaviors. We test this framework with a publicly available dataset generated from an autonomous aerial vehicle (UAV) application and the results show that by creating a fine-grained model that exploits previously unharnessed properties of the system calls, we can create a dynamic anomaly detection framework that evolves as the threats change.
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 | 0.001 |
| 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 it