Measuring Intrusion Impacts for Rational Response: A State-based Approach
Bibliographic record
Abstract
Although intrusion detection systems (IDSs) are playing significant roles in defending information systems against attacks, they can only partially reflect the true system states due to false alarms, low detection rate, inaccurate reports, and inappropriate responses. Automated response component built upon such systems therefore must consider the imperfect picture inferred from them and take actions accordingly. This paper presents a stat- based approach to measuring intrusion impacts on the basis of IDS reports, and analyzing costs and benefits of response polices supposed to be taken. Specifically, assuming the system evolves as a Markov process conditioned upon the current system state, imperfect observation and action, a partially observable Markov decision process to model the efficacy of IDSs (as well as alert correlation technology) as providing a probabilistic assessment of the state of system assets, and to maximize rewards (cost and benefit) by taking appropriate actions in response to the estimated states. The objective is to move the system towards more secure states with respect to particular security metrics. We use a real trace benchmark data to evaluate our approach, and demonstrate its promising performance.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".