An Anomaly Detection Model for IoT Networks based on Flow and Flag Features using a Feed-Forward Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
The security of IoT networks is becoming increasingly challenging, and anomaly detection for IoT network traffic is a critical technique for addressing this issue. However, extracting precise and effective network traffic features for anomaly detection is challenging. To address this issue, the current research analyzes various types of network flow features. In this paper, we present the design and development of an anomalous activity detection system for IoT networks based on flow and control flags features using a feed-forward neural network. The model has been evaluated using BoT-IoT, IoT network intrusion, MQTT-IoT-IDS2020, MQTTset, IoT-23, and IoT-DS2 datasets for binary and multiclass classification. Our proposed binary and multiclass classification model attained high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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