
Novel secure positioning method in ultra‐wide band framework based on overshadowing attack probabilistic model
Author(s) -
Orouji Niloofar,
Mosavi Mohammad Reza
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.1138
Subject(s) - computer science , probabilistic logic , position (finance) , internet of things , mean squared error , key (lock) , computer security , ranging , signal (programming language) , process (computing) , power (physics) , algorithm , real time computing , artificial intelligence , telecommunications , statistics , mathematics , physics , finance , quantum mechanics , economics , programming language , operating system
The extensive use of Internet of things (IoT) devices demonstrates an increasing demand for secure frameworks. Position altering attacks are amongst the most severe vulnerabilities in two‐way time of arrival distance measurement technique, which is the most common in distance measurements by IoT devices. The overshadowing attack as a distance enlargement attack replays the authentic signal with a certain amount of delay and sensible power amplification. Therefore, the attack detection is difficult and requires a precise model to estimate its behaviour. The contribution of this study is a location estimation method based on the overshadowing attack probabilistic model that estimates the attack outcome delay and corrects the erroneous measurements. The overshadow delay, which is the delay between the authentic signal and the attacked one, plays a key role in this process. The method has been evaluated by three attack scenarios and compared to the conventional maximum likelihood (CML) and mixture secure location of things (M‐SLOT) methods. The results of the proposed method demonstrate root mean squared error (RMSE) of 0.48 in position estimation. With the same dataset, RMSEs of M‐SLOT and CML methods are 4.69 and 9.84, respectively, which confirm the better performance of the proposed method.