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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 23; p. 6756
• Main Authors: Ko, DongHyun, Choi, Seok-Hwan, Ahn, Sungyong, Choi, Yoon-Ho
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.11.2020; MDPI
• 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; MAC spoofing attack; indoor localization system; convolutional neural network; received signal strength; indoor localization; random forest; fingerprinting
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20236756

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.