The proliferation of the Internet of Things (IoTs) and pervasive use of many different types of mobile computing devices make wireless communication spectrum a precious resource. In order to accommodate the still fast increasing number of devices requesting wireless connection, more efficient, fine-grained spectrum allocation and sharing schemes are badly in need. Cognitive radio networks (CRNs) have been widely recognized as one promising solution, in which the secondary users (SUs) are allowed to share channels with licensed primary users (PUs) as long as bringing no interference to the normal operations of the PUs. However, malicious attackers or selfish SUs may mimic the behavior of PUs to occupy the channels illegally. It is nontrivial to accurately, timely detect such kind of primary user emulation (PUE) attacks. In this paper, an efficient PUE attacked detection method is introduced leveraging the recurrent neural network (RNN). After a fundamental algorithm using basic RNN, an advanced version taking advantage of long-short-term-memory (LSTM) is proposed, which is more efficient on processing time series with long term memory. The experimental study has provided deeper insights about the different performances the RNNs achieved and validated the effectiveness of the proposed detectors.
Some literatures have explored different ways using activity pattern for PUE attack detection. One of the recent work explored a cooperative spectrum sensing method on detection of PUE attack @cite_10 . The author considered the most common spectrum sensing approach, energy detection, and find the optimal detection threshold of the aggregated sensing samples from all CRs by using numerical method. The proposed approach is easy to be implemented for its low cost, and it is also compatible to practical spectrum sensing process. However, the proposal is weak in several aspects: a) to find the optimal detection threshold, the prior probabilities of attack are assumed known; b) the sensed samples are assumed to follow the Gaussian distribution, which is not proven; and c) the attackers in the model are assumed too passive to adjust their behavior, such as attack strength, attack probability and attack timing. As a matter of fact, a smart attacker will easily deceive such detector.
@cite_10
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Abstract Cooperative spectrum sensing (CSS) is an effective approach to improve the detection performance in cognitive radio (CR) networks. The CSS process imposes some additional security threats to the CR networks. One of the common threats is primary user emulation attack (PUEA). In this particular type of attack, some malicious users try to imitate primary signal characteristics and defraud CR users to prevent them from accessing the idle frequency bands. Here, we propose a novel cooperative sensing scheme with attack-aware capability in the presence of a malicious PUEA. More precisely, the probabilities of the presence of a PUEA fake signal in both presence and absence of the licensed PU signal are estimated. Then, the obtained parameters are used to determine the optimal threshold that minimizes the total error probability. Simulation results are presented to indicate the performance improvement of the proposed method against PUEA compared with the conventional method.