| Title | Machine Learning Bluetooth Profile Operation Verification via Monitoring the Transmission Pattern |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Elkanishy, Abdelrahman, Badawy, Abdel-Hameed A., Furth, Paul M., Boucheron, Laura E., Michael, Christopher P. |
| Conference Name | 2019 53rd Asilomar Conference on Signals, Systems, and Computers |
| Date Published | nov |
| Keywords | Blue-tooth, Bluetooth, Bluetooth profile operation verification, bluetooth security, Bluetooth SoC, Bluetooth System-on-Chip, classifier, communication complexity, composability, custom low-frequency integrated circuit, Cyber physical system, cyber physical systems, Decision Tree, electronic engineering computing, feature extraction, formal verification, hardware security, hardware-software security, Human Behavior, integrated circuit manufacture, k-nearest neighbor, learning (artificial intelligence), license communication IC, low computational complexity, low-cost legacy technology, machine learning, mobile radio, profile classification algorithm, pubcrawl, radio frequency output power, resilience, Resiliency, RF envelope detector, RF output power, RF Power, security, signal classification, smart descriptive time-domain feature extraction, Supervisory Circuit, support vector machine, system-on-chip, telecommunication computing, telecommunication security, transmission pattern |
| Abstract | Manufacturers often buy and/or license communication ICs from third-party suppliers. These communication ICs are then integrated into a complex computational system, resulting in a wide range of potential hardware-software security issues. This work proposes a compact supervisory circuit to classify the Bluetooth profile operation of a Bluetooth System-on-Chip (SoC) at low frequencies by monitoring the radio frequency (RF) output power of the Bluetooth SoC. The idea is to inexpensively manufacture an RF envelope detector to monitor the RF output power and a profile classification algorithm on a custom low-frequency integrated circuit in a low-cost legacy technology. When the supervisory circuit observes unexpected behavior, it can shut off power to the Bluetooth SoC. In this preliminary work, we proto-type the supervisory circuit using off-the-shelf components to collect a sufficient data set to train 11 different Machine Learning models. We extract smart descriptive time-domain features from the envelope of the RF output signal. Then, we train the machine learning models to classify three different Bluetooth operation profiles: sensor, hands-free, and headset. Our results demonstrate 100% classification accuracy with low computational complexity. |
| DOI | 10.1109/IEEECONF44664.2019.9048970 |
| Citation Key | elkanishy_machine_2019 |
Machine Learning Bluetooth Profile Operation Verification via Monitoring the Transmission Pattern