FTLIoT: A Federated Transfer Learning Framework for Securing IoT
Why this work is in the frame
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Bibliographic record
Abstract
The growing number of Internet of Things (IoT) applications and connected devices has increased the chance for more cyberattacks against those applications and devices and emphasized the need to protect the IoT networks. Due to the vast network and the anonymity of the internet, it has been challenging to preserve private information and communication. Although most systems implement security devices (i.e. firewalls) to avoid this, the second line of defence, Intrusion Detection Systems (IDSs), are critical in enhancing the system's security level. This paper proposed a model that combines the two machine learning techniques, Federated and Transfer Learning, to build an IDS to secure the IoT networks with less training time and enhanced performance while preserving the user's data privacy. Deep learning algorithms, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), are used to evaluate the performance of the proposed framework on a benchmark dataset, CSE-CIC-IDS2018, and the feasibility of adopting Federated Transfer Learning (FTL) is shown in terms of performance metrics and training and fine-tuning time. The results show that the proposed technique can increase performance and decrease training time compared to the traditional machine learning techniques.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 it