An Intrusion-Tolerant Mechanism for Intrusion Detection Systems
Bibliographic record
Abstract
In accordance with the increasing importance of intrusion detection systems (IDS), users justifiably demand the trustworthiness of the IDS. However, sophisticated attackers attempt to disable the IDS before they launch a thorough attack. Therefore, to accomplish its function, an IDS should have some mechanism to guarantee uninterrupted detection service even in the face of IDS component failures due to attacks. In this paper, we propose an intrusion-tolerant mechanism for network intrusion detection systems (NIDS) that employ multiple independent components. The mechanism monitors the detection units and the hosts on which the units reside and enables the IDS to survive component failure due to intrusions. As soon as a failed IDS component is discovered, a copy of the component is installed to replace it and the detection service continues. We implement the intrusion-tolerant mechanism based on the CSI-KNN-based NIDS and evaluate the prototype in the face of component failures. The results demonstrate that the mechanism can effectively tolerate intrusions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".