# Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
## 3 Experiments and Results
Figure 7 shows the probability of miss detection as a function of the distance \(\mathrm{d}_{\mathrm{pu\_pue}}\) between a mobile primary user and a primary user emulation attacker. As expected, the probability of miss detection decreases as the distance between the PU and the PUE increases. For example, for an SNR value of \(-10\) dB, the probability of miss detection is equal to 65% when the distance between the primary user and the attacker is equal to 50 m, but this probability drops to 41% when the distance increases to 100 m. In addition, this figure shows that when the SNR value increases, the probability of miss detection decreases. For example, with a distance of 110 m between the primary user and the  
Figure 5: Probability of detection as a function of the distance \(\mathrm{d}_{\mathrm{PU\_PUE}}\) between a mobile PU and a PUE. Figure 6: Probability of detection as a function of the probability of false alarm for \(\mathrm{d}_{\mathrm{PU\_PUE}}=30\) m.  
attacker, the probability of miss detection is equal to 37%, 10%, 5%, 1%, and 0% for SNR values of \(-10\) dB, \(-5\) dB, 0 dB, 5 dB, 10 dB, respectively. Figure 8 shows a comparison between the proposed approach and the RSS-based localization technique in terms of the probability of detection as a function of distance with an SNR value of \(-10\) dB. The RSS-based localization technique is selected for comparison purpose since it has been proposed in several previous papers [20; 23; 24]. This technique uses the transmitter received power to derive the position of the transmitter. The calculated position is combined with other techniques including Particle Swarm Optimization and the Maximum Likelihood estimation to optimize the detected location of the transmitter. This technique meets the Federal Communication Commission which states that no modification to the primary user system should be performed to accommodate the opportunistic use of the spectrum by secondary users. As shown in this figure, the probability of detection for the proposed approach, as well as the RSS-based localization approaches, is low when the attacker is in a close proximity to the legitimate primary user location. When the distance increases, the probability of detection of the proposed approach increases while it remains almost the same for the RSS-based localization technique. For instance, when the distance between the primary user emulation attacker and the primary user is equal to 50 m, the probabilities of detection of the proposed approach and the RSS-based localization technique are 24% and 16%, respectively. When the distance increases to 100 m, the probability of detection of the proposed approach increases to 49% while it remains equal to 16% for the RSS-based localization approach. This result suggests that it is challenging to detect the primary user emulation attacker when the attacker is in a close proximity to the legitimate primary user location. However, when the distance becomes larger, the proposed approach produces better results than the RSS-based approach because in this latter, the secondary users use the received signal strength to calculate the position of the transmitter then compare it to the fixed and known position of the legitimate primary user. Since the position of the primary user changes over time, this RSS-based approach produces inaccurate results in terms of the probability of detection. Figure 7: Probability of miss detection as a function of the distance \(d_{\text{PU\_PUE}}\) between the mobile PU and a PUE. Figure 9 illustrates a comparison between the proposed technique and the RSS-based localization technique in terms of the probability of miss detection as a function of the distance with an SNR value equal to \(-10\) dB. As one can see, the miss detection probabilities of the proposed approach and the RSS-based localization technique are high when the attacker is close to the primary user location.