Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks

Abstract

The Mobile Cognitive Radio Network (MCRN) are an alternative to spectrum scarcity. However, like any network, it comes with security issues to analyze. One of the attacks to analyze is the Primary User Emulation (PUE) attack, which leads the system to give the attacker the service as a legitimate user and use the Primary Users’ (PUs) spectrum resources. This problem has been addressed from perspectives like arrival time, position detection, cooperative scenarios, and artificial intelligence techniques (AI). Nevertheless, it has been studied with one PUE attack at once. This paper implements a countermeasure that can be applied when several attacks simultaneously exist in a cooperative network. A deep neural network (DNN) is used with other techniques to determine the PUE’s existence and communicate it with other devices in the cooperative MCRN. An algorithm to detect and share detection information is applied, and the results show that the system can detect multiple PUE attacks with coordination between the secondary users (SUs). Scenarios are implemented on software-defined radio (SDR) with a cognitive protocol to protect the PU. The probability of detection (PD) is measured for some signal-to-noise ratio (SNR) values in the presence of one PUE or more in the network, which shows high detection values above 90% for an SNR of -7dB. A database is also created with the attackers’ data and shared with all the SUs.