Anomaly-based intrusion detection using mobility profiles of public transportation users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For the purpose of anomaly-based intrusion detection in mobile networks, the utilization of profiles, based on hardware signatures, calling patterns, service usage, and mobility patterns, have been explored by various research teams and commercial systems, namely the fraud management system by Hewlett-Packard and Compaq. This paper examines the feasibility of using profiles, which are based on the mobility patterns of mobile users, who make use of public transportation, e.g. bus. More specifically, a novel framework, which makes use of an instance based learning technique, for classification purposes, is presented. In addition, an empirical analysis is conducted in order to assess the impact of two key parameters, the sequence length and precision level, on the false alarm and detection rates. Moreover, a strategy for enhancing the characterization of users is also proposed. Based on simulation results, it is feasible to use mobility profiles for anomaly-based intrusion detection in mobile wireless networks.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it