# Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
## 1 Introduction
Other techniques have been proposed to estimate the position of the transmitter and compare the estimated position with the known position of the primary user. The authors of Reference [19] proposed a time difference of arrival (TDoA) based approach to estimate the position of the transmitter. In this approach, the time elapsed between the transmission of the signal and the reception of the reply is used to estimate the location of the transmitter. Though TDoA can estimate more accurately the transmitter position than other techniques, it requires a tight synchronization between the transmitters and the receivers, which is challenging. In Reference [20], the authors proposed an angle of arrival based approach for detecting the transmitter's position. In this approach, the direction of the received signal is measured at different reference nodes, then by applying the triangulation technique, the transmitter location can be estimated. However, this technique is affected by the multipath phenomenon [21]. The authors of Reference [22] proposed a mitigation approach to distinguish the primary user signal from other signals via an energy-efficient localization technique and channel parameter variance. The authors of Reference [23] proposed a model based on the trilateration, the received signal strength (RSS), and the particle swarm optimization to increase the detection accuracy of the primary user emulation attacker. All the previously mentioned techniques do not deal with uncertainty which affects the measurements. The authors of Reference [24] proposed a Bayesian model and trilateration technique for detecting the primary user emulation attack position. Based on the received signal strength, the Bayesian decision theory is used to deal with the uncertainty related to the primary user environment and increase the detection accuracy of the primary user position. The existing localization techniques assume that the primary user position is fixed and known. Thus, each secondary user derives the position of the transmitter based on the received power and then compares it to the known position of the legitimate PU in order to verify if the transmitter is the legitimate PU or an attacker. However, in wireless communication networks where the primary user is mobile, such as cognitive radio ad hoc networks, the existing techniques produce inaccurate results since the position of the primary user varies over time. Therefore, in this paper, we propose a localization approach to detect the primary user emulation attacks in the case of mobile primary users. The proposed approach is based on the Kalman filter framework for tracking the position of the primary user then the transmitter received power is used to derive the position of the transmitter before comparing it to the position estimated by Kalman filter. The Kalman filter has been applied in several contexts including robotics vision, wireless sensor network, and tracking moving objects [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. For instance, in Reference [25], the authors used the Kalman filter for robot mobile localization purpose. In Reference [26], the Kalman filter was applied for face tracking; and in Reference [27] the authors used the Kalman filter for tracking balls in the robotics vision field. Other applications of the Kalman filter have been proposed in the field of wireless sensor networks. For example, in Reference [28], the authors proposed an approach based on the Kalman filter combined with the Maximum Likelihood Estimation for tracking nodes in wireless sensor networks. The authors of Reference [29] proposed a routing protocol called SEARCH that predicts the destination in advance using the Kalman filter so that the route is extended and packets are reliably delivered to their destinations. In Reference [30], the authors used the Kalman filter for tracking the Global Navigation Satellite System signal.