Abstract
Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.
Highlights
The constant evolution of services and applications, the Internet of things, and the need for more bandwidth in wireless networks lead us to develop, implement and improve technologies such as mobile cognitive radio networks (MCRN), which helps to manage the spectrum scarcity problem [1].The general concept of cognitive radio is an intelligent communication system that adapts in real-time to the radio environment; it is flexible and makes a better use of frequency resources
A malicious primary user emulation (PUE) detector was proposed based on an support vector machine (SVM) with Rényi entropy analysis of energy received for an MCRN
The system was examined in an software-defined radio (SDR) scenario by using USRP NI-2922 configured with Gaussian minimum shift keying (GMSK) and orthogonal frequency division multiplexing (OFDM) modulations, and the expected results were simulated with Monte
Summary
The general concept of cognitive radio is an intelligent communication system that adapts in real-time to the radio environment; it is flexible and makes a better use of frequency resources For this task, it continuously senses the radio frequency environment to find spectral holes where the primary user (PU) is not transmitting, and it involves the identification of PU activity in the spectrum and frequency hopping in case PU signal detected [2]. The main objective is to detect if there is a PU signal at a specific frequency, and the system must do this in real-time so that the algorithms are not complex and have a good time response [1] Due to this requirement, a specific attack on the MCRN appears. The objective of the attacker in a malicious attack is to obtain control of the channel while avoiding any user of it—it does not transmit information or communicate by itself, but it is hard to distinguish between a PU or a PUE [4]
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