Bibliographic record
Abstract
Due to the rapid and continuous increase of network intrusion, the need to protect computer systems and underlying infrastructure becomes inevitable. Beside this, the systems have additionally gotten extremely intricate as they fill in both scale and usefulness;hence,intrusion/anomaly detection becomes essential. The intrusion or anomaly detection poses several challenges including data collections due to the inherent datasets imbalance, caused by systems' reliability requirements causing the event of an anomaly a irregularity phenomenon. Therefore, only a small percentage of available datasets captures the anomaly, which brings in the second challenge, i.e, model selection, and a specific approach for detecting an anomaly. While much research has been concentrated on the data collection part and statistical techniques, the focus of this work is devoted to a multi-module system call anomalies detection technique. We propose a novel approach based on Long Short Term Memory(LSTM) and attention using transformers that can learn a sequence of a system call efficiently. Experimental results showed that the proposed deep learning model is 92.6% precise with a recall of 93.8% to classify the malicious process in the system.
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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 | 0.000 |
| 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".