Authors:
Ahmet Aksoy
;
Sundeep Varma
;
Ganesha Moorthy
;
Enya Pan
and
Gorkem Kar
Affiliation:
Department of Computer Science and Cybersecurity, University of Central Missouri, Warrensburg, MO, 64093, U.S.A.
Keyword(s):
Genetic Algorithms, Ant Colony Optimization, Artificial Bee Colony, Feature Selection Algorithms, Filter Methods, Wrapper Methods, Embedded Methods, Machine Learning, IoT Device Fingerprinting.
Abstract:
IoT devices are increasingly becoming a part of our daily lives. As such, there is a growing emphasis on enhancing their security, which will also ensure the security of the networks to which they belong. Identifying and isolating vulnerable devices from the network is crucial to increase overall security. In this paper, we demonstrate the contribution of various feature selection algorithms used with Decision Tree classifiers to the problem of detecting vendors and types of IoT devices. We use a single TCP/IP packet originating from each device and utilize their packet header field values to capture their unique fingerprints automatically. We compare several algorithms from the Filter, Wrapper, Embedded, and Search Optimization domains of feature selection and indicate which works best for individual scenarios. We utilize the IoT Sentinel dataset and achieve 95.3% accuracy in classifying 126,209 unique TCP/IP packets across various vendors of devices using weighted accuracy and 88.7
% accuracy using macro accuracy, which is the average of F1-Scores of all vendors in the dataset.
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