Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers
Abstract
:1. Introduction
- Since [8] evaluates the above classifiers as a module of the whole sniffing system, only per-channel classification results are available. However, since AFH sequence relies on the resulting AFH map, per-channel classification results is not sufficient to evaluate contribution of the classifier in packet sniffing.
- Although SVM is known as powerful on a wide range of problems, there are broad categories of machine learning classifiers. We could understand the AFH map prediction problem more comprehensively by comparing results from multiple machine learning classifiers.
2. Related Work
3. Backgrounds
3.1. An Overview of Bluetooth and AFH
3.2. Machine Learning Classifiers
3.2.1. Logistic Regression
3.2.2. Decision Tree
3.2.3. Random Forest
3.2.4. Support Vector Machine
3.2.5. Multi-Layer Perceptron
4. Experiment
4.1. AFH Map Collection and Prediction Testbed
4.2. Spectrum Sensing
4.3. Data Processing and the Resulting Dataset
4.4. Training and Evaluation
5. Evaluation Results
5.1. Per-Channel Classification
5.2. AFH Map Prediction
5.3. Discussion
5.4. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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# of 0 | # of 1 | Total | # of AFH Map | # of 0 | # of 1 | Total | # of AFH Map | ||
---|---|---|---|---|---|---|---|---|---|
A | 16,743 | 83,824 | 100,567 | 1273 | Training set | 17,797 | 89,011 | 105,808 | 1352 |
B | 1054 | 5187 | 6241 | 79 | |||||
C | 4572 | 16,363 | 20,935 | 265 | Test set | 4572 | 16,363 | 20,935 | 265 |
Actual Class | ||||
---|---|---|---|---|
P | N | Sum | ||
Predicted class | P | 16,143 | 3840 | 19,983 |
N | 220 | 732 | 952 | |
sum | 16,363 | 4572 | 20,935 |
Actual Class | ||||
---|---|---|---|---|
P | N | Sum | ||
Predicted class | P | 16,363 | 4572 | 20,935 |
N | 0 | 0 | 0 | |
sum | 16,363 | 4572 | 20,935 |
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Lee, J.; Park, C.; Roh, H. Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers. Energies 2021, 14, 928. https://doi.org/10.3390/en14040928
Lee J, Park C, Roh H. Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers. Energies. 2021; 14(4):928. https://doi.org/10.3390/en14040928
Chicago/Turabian StyleLee, Janggoon, Chanhee Park, and Heejun Roh. 2021. "Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers" Energies 14, no. 4: 928. https://doi.org/10.3390/en14040928