Abstract
The introduction of cognitive radio enables dynamic spectrum access for higher spectrum utilization, due to its ability of awareness of their environment. However, the introduction of cognitive radio technology brings new challenges to wireless networks security. Due to intelligent nature of the attackers, many of the radio frequency jamming attacks can be stealthy by nature. The stealthy jamming attacks can significantly impact the performance of the wireless communication system and can lead to significant overhead in terms of retransmission and increment of power consumption. This paper presents a new physical layer approach for stealthy jammer detection in wide-band (WB) cognitive radio networks. The proposed algorithm consider a WB consists of multiple narrow-band sub-bands (SB), which can be occupied by licit or jamming signals. The cyclostationary spectral analysis is performed on this WB signal to compute spectral correlation function (SCF). The alpha profile is extracted from the SCF and used as input features to artificial neural network (ANN), which classify each NB signal as a licit signal or a jamming signal. In the end, the performance of the proposed approach is shown with the help of Monte-Carlo simulations under different empirical setups.