Edge Intelligence (EI)-Enabled HTTP Anomaly Detection Framework for the Internet of Things (IoT)
Bibliographic record
Abstract
In recent years, with the rapid development of the Internet of Things (IoT), various applications based on IoT have become more and more popular in industrial and living sectors. However, the hypertext transfer protocol (HTTP) as a popular application protocol used in various IoT applications faces a variety of security vulnerabilities. This article proposes a novel HTTP anomaly detection framework based on edge intelligence (EI) for IoT. In this framework, both clustering and classification methods are used to quickly and accurately detect anomalies in the HTTP traffic for IoT. Unlike the existing works relying on a centralized server to perform anomaly detection, with the recent advances in EI, the proposed framework distributes the entire detection process to different nodes. Moreover, a data processing method is proposed to divide the detection fields of HTTP data, which can eliminate redundant data and extract features from the fields of an HTTP header. Simulation results show that the proposed framework can significantly improve the speed and accuracy of HTTP anomaly detection, especially for unknown anomalies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".