Network Flow Classification and Volume Prediction using Novel Ensemble Deep Learning Architectures in the Era of the Internet of Things (IoT)
Bibliographic record
Abstract
Network flow and volume optimization have become a challenging task with the rapid growth of IoT devices. Fortunately, network flow classification and network volume prediction techniques are powerful tools to allocate resources efficiently in a given network. Current machine learning models have proven useful since they partition and direct traffic to extremely diverse sets of devices and services. Current machine/deep learning techniques are geared towards specific network configurations and suffer from suboptimal accuracy or lengthy training methodologies. In this paper, we propose a family of novel ensemble techniques for short-term network volume predictions and flow classifications. We explore each architecture and the characteristics of each model to achieve high efficiency. Experimental results on three real network traffic datasets show that the proposed family of deep ensemble models using Long Short-Term Memory (LSTM) and Convolutional LSTM neural network (CNN_LSTM) are best-in-class models for short-term flow classification and volume predictions.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".