An HTTP Anomaly Detection Architecture Based on the Internet of Intelligence
Bibliographic record
Abstract
The prompt expansion of the Internet of Things (IoT) and its wide application in smart homes and transportation has brought tremendous convenience to people’s lives. However, the increase of IoT devices has also brought huge security problems, threatening people’s information and property security. This paper designs a new anomaly detection architecture based on the concept of the “Internet of intelligence”. It is a general architecture that can be applied to different IoT anomaly detection methods. The architecture effectively combines the blockchain and the IoT anomaly detection method, which can overcome the problems of data resource sharing and collective learning. At the same time, we propose a novel method for detecting abnormal HTTP traffic in IoT. It combines clustering and Autoencoder method to efficiently and exactly detect abnormal HTTP traffic in IoT devices. In addition, we propose an optimized feature extraction method, which is favorable to enhance the detection effect. Simulation results show the proposed architecture and method can enhance the detection performance of abnormal HTTP traffic in IoT and address the challenges of existing approaches.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".