Device Identification and Anomaly Detection in IoT Environments
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
As the Internet of Things (IoT) landscape continues to expand, a diverse range of devices with various functionalities is being integrated into the IoT ecosystem. When traditional systems, which involve human interaction, are replaced by devices, it becomes crucial to upgrade the conventional authorization and authentication mechanisms. Traditional approaches for device identification and anomaly detection often fail to address the dynamic behaviors of IoT devices due to the highly heterogeneous nature of the IoT environment. To address these challenges, this article proposes a novel and lightweight integrated model for simultaneous IoT device identification and anomaly detection. The proposed approach leverages both packet-based and flow-based feature extraction techniques to extract a diverse and significant set of features, which are crucial for robust anomaly detection and device classification. This novel combined feature set incorporates a wide range of attributes from various domains, including HTTPS-related features, handshake information, and user agent strings, specifically extracted for IoT device identification. In addition, the feature set includes specialized attributes for anomaly detection, such as stream, channel, and jitter metrics, which are calculated over different time intervals to enhance the model’s anomaly detection capabilities. Experimental analysis, conducted using real network traffic data from state-of-the-art datasets, demonstrates the model’s efficiency and scalability, which makes the model well-suited for real-time IoT threat detection and device management in resource-constrained environments.
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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 | 0.000 |
| 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