A Multi-agent-based Approach to Improve Intrusion Detection Systems False Alarm Ratio by Using Honeypot
Bibliographic record
Abstract
In this paper we propose a new architecture, which is composed of distributed cooperative agents to reduce the false alarm ratio of the intrusion detection systems (IDS) in a twofold contribution. The first contribution lies in reducing the false alarm rate of the attack detection in an agent-based architecture by using honeypot network as the closer level of investigation. The connection is retrieved to the original destination in case of false alarm recognition, while the actions are hidden to the user. Such a scheme significantly decreases the alarm rate and provides a higher performance of IDS. The second contribution applies the game theoretic analysis in the sense that the contributing agents are led to perform the best they could in order to achieve their goals. The Shaply value is computed to find the actual contribution of each agent in the coalition he belongs to. The equilibrium point is found and consequently the winner coalition is formed. In this paper the architecture of the proposed system is described, a theoretical analysis of agents' behavior is given and its possible extensions are explained.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".