A controller-based autonomic defense system
Bibliographic record
Abstract
We demonstrate the results of our research into the implementation of a host-based autonomic defense system (ADS) using a partially-observable Markov decision process. The goal of an ADS is to "relexively" respond to an attack, thwarting it to the extent that humans have time to form a tactical response to the attack. A defensive system that automatically responds to an attack must meet two criteria: it must select the correct response in the face of an attack, and it must not take actions to attacks that are not there. This challenge is exacerbated by the fact that, in order to detect never-before-seen attacks, the ADS must use anomaly detectors for its sensor input; anomaly detectors typically have relatively high false positive and false negative rates. Thus, key to an ADS is a controller that can obtain a valid signal from a noisy sensor. The ALPHATECH Lightweight Autonomic Defense System (/spl alpha/LADS) is a prototype ADS constructed around a PO-MDP stochastic controller. The state model allows the controller to filter out the false positives from the anomaly sensor such that authorized processes are not killed and false alerts are not issued. We demonstrate /spl alpha/LADS defending against Internet worms operating in real time.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".