A Sliding Window Based Management Traffic Clustering Algorithm for 802.11 WLAN Intrusion Detection
Bibliographic record
Abstract
This paper introduces a novel Management Traffic Clustering Algorithm (MTCA) based on a sliding window methodology for intrusion detection in 802.11 networks Active attacks and other network events such as scanning, joining and leaving in 802.11 WLANs can be observed by clustering the management frames in the MAC Layer. The new algorithm is based on a sliding window and measures the similarity of management frames within a certain period by calculating their variance. Through filtering out certain management frames, clusters are recognized from the discrete distribution of the variance of the management traffic load. Two parameters determine the accuracy and robustness of the algorithm: the Sample Interval and the Window Size of the sliding window. Extensive tests and comparisons between different sets of Sample Intervals and Window Sizes have been carried out. From analysis of the results, recommendations on what are the most appropriate values for these two parameters in various scenarios are presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".