Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environmen...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-11, Vol.20 (23), p.6756
Main Authors: Ko, DongHyun, Choi, Seok-Hwan, Ahn, Sungyong, Choi, Yoon-Ho
Format: Article
Language:English
Subjects:
Access control
Accuracy
Algorithms
Art galleries & museums
Datasets
Deep learning
fingerprinting
indoor localization
indoor localization system
Letter
Localization method
MAC spoofing attack
Methods
Neural networks
Noise
Noise prediction
random forest
received signal strength
Spoofing
Wireless access points
Wireless networks
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Summary:With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20236756