TY - GEN
T1 - Computationally-Efficient Secured IoT Networks
T2 - 2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
AU - Abdallah, Abdallah S.
AU - Vieira, Flavio H.T.
AU - Cardoso, Kleber V.
AU - Zeng, Zheng
AU - Hemminger, William
AU - Abreu, Marcos de
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The vulnerability of wireless devices to a well-known set of probable cyberattacks has made safeguarding the networks to which these devices connect a tremendous security issue, threatening the safety and security of thousands, if not millions, of private and public networks. Due to the rapid growth of embedded and wearable wireless devices on the market, wireless Internet of Things (IoT) devices are now one of the most vulnerable entry points because they don't have advanced authentication procedures. This article provides a summary of our most recent findings in the development of a novel authentication and identification method for IoT ZigBee and Long Range (LoRa) devices based on the physical signals they emit. Our method relies on the extraction of a collection of unique features from the received modulated signal in order to construct a features vector for each device and then train a machine learning model using the acquired features. Following training, the trained model is evaluated by testing its ability to identify and recognize the authorized devices (i.e., those previously included in the training set) from the testing set, which contains an evenly distributed random mix of new and authorized devices. Our method employs differential constellation trace Figure (DCTF)-based features for the features vector and computationally-efficient machine learning methods, such as Quadratic Discriminant Analysis (QDA) and Gaussian Naive Bayes classifiers, which resulted in a recognition accuracy greater than 90 percent.
AB - The vulnerability of wireless devices to a well-known set of probable cyberattacks has made safeguarding the networks to which these devices connect a tremendous security issue, threatening the safety and security of thousands, if not millions, of private and public networks. Due to the rapid growth of embedded and wearable wireless devices on the market, wireless Internet of Things (IoT) devices are now one of the most vulnerable entry points because they don't have advanced authentication procedures. This article provides a summary of our most recent findings in the development of a novel authentication and identification method for IoT ZigBee and Long Range (LoRa) devices based on the physical signals they emit. Our method relies on the extraction of a collection of unique features from the received modulated signal in order to construct a features vector for each device and then train a machine learning model using the acquired features. Following training, the trained model is evaluated by testing its ability to identify and recognize the authorized devices (i.e., those previously included in the training set) from the testing set, which contains an evenly distributed random mix of new and authorized devices. Our method employs differential constellation trace Figure (DCTF)-based features for the features vector and computationally-efficient machine learning methods, such as Quadratic Discriminant Analysis (QDA) and Gaussian Naive Bayes classifiers, which resulted in a recognition accuracy greater than 90 percent.
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U2 - 10.1109/ICECET55527.2022.9872952
DO - 10.1109/ICECET55527.2022.9872952
M3 - Conference contribution
AN - SCOPUS:85138858002
T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2022 through 22 July 2022
ER -