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Cognitive radio (CR) is an enabling technology for future wireless spectrum allocation to improve the use of licensed spectrum by enabling unlicensed users equipped with CRs to coexist with incumbent users in licensed spectrum bands while causing no interference to incumbent communication. However, security challenges faced by CR technology are still a research topic. One of the prevailing challenges, is the radio frequency jamming attack , where adversaries are able to exploit on-the-fly reconfigurability potentials and learning mechanism of cognitive radios in order to devise and deploy advanced jamming tactics. These attacks can significantly impact the performance of wireless communication systems and lead to significant overheads in terms of retransmission and increment of power consumption. In this work, a new jammer detection algorithm is proposed for wide-band (WB) radios. The proposed approach assumes a WB spectrum occupied by various narrow-band (NB) signals, which can be either legitimate or jamming signals. First, the received WB signal is recovered from sub-Nyquist rate samples using compressed sensing. Compressed sensing is used to alleviate Nyquist rate sampling requirements at the receiver A/D converter. After the Nyquist rate signal has been recoverd, a cyclostationary spectral analysis is performed on this estimated WB signal to compute spectral correlation function (SCF). The alpha profile is then extracted from SCF and used to classify each NB signal as a licit signal or illicit signal. In the end, the performance of the algorithm is shown with the help of Monte-Carlo simulations under different empirical setups.