An Experience Improving Intrusion Detection Systems False Alarm Ratio by Using Honeypot
Bibliographic record
Abstract
When traditional firewall and intrusion detection systems (IDS) are used to detect possible attacks from the network, they often make wrong decisions and block the legitimate connections. In this paper we propose a new architecture which is composed of distributed agents and honeypot. The main focus of our approach lies in reducing the false alarm rate of the attack detection. Using the honeypot scheme, this system is able to avoid many wrong decisions made by IDS. In this system alarming adversaries, initially detected by the IDS, will be rerouted to a honeypot network for a more close investigation. If as a result of this investigation, it is found that the alarm decision made by the IDS of the agent is wrong, the connection will be guided to the original destination in order to continue the previous interaction. This action is hidden to the user. Such a scheme significantly decreases the alarm rate and provides a higher performance of IDS. In this paper the architecture of the proposed system is described, a theoretical analysis of its behavior is given and its possible extension and implementation are explained.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".