)1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS] (N.N. ); [EMAIL_ADDRESS] (R.J.L. )1 LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; neda. Several spectrum sensing techniques, such as Cyclo-Stationary Feature Detection (CFD) [7], Energy Detection (ED) [8], and matched filtering-based detection [9], have been proposed for RFI monitoring in Cognitive Radios (CR) [10]. Among these techniques, CFD [7] can recognize the Sol from interference and noise, even in very low SNR contexts, but its implementation is computationally very complex [11]. On the other hand, ED is straightforward to deploy but is unreliable in cases of in-band interference and very noisy conditions [11]. As the matched filtering detector is highly dependent on the demodulation of a primary user signal, for its part, it requires a dedicated receiver design, which may not be possible in a practical CR terminal [12]. More significantly, the techniques above can only determine the presence and not the type of interference. To address this, Artificial Intelligence (AI)-based methods have been employed and found as a superior to traditional approaches since classification techniques may not only determine the different kinds of RFI but also specify the modulation types efficiently. In most of the related studies, the authors focus on either determining the various types of RFI or the modulation types of Sol without considering the presence of the jamming signal. In this work, we propose a novel approach based on hierarchical classification design to recognize not only the types of the received signal and Automatic Modulation Classification (AMC). To this end, we have considered modulation recognition under two severe conditions such as channel noise and the existence of various jamming signals. Moreover, we have generated a new real-time RFI dataset in which SoI is a video stream transmitted with four modulation types (QPSK, 8 APSK, 16 APSK, and 32 APSK) in a real-time direct satellite-to-ground communication based on DVB-S2 standards. Furthermore, the studied Sol is combined with one of the three major jamming signal types, namely CWI, MCWI, and CI. Notably, we have studied the effect of these three jamming signals on RFI classification and AMC because they are very simple to be deployed by adversaries. As feature selection is the previous step to classification in supervised learning, we have developed two feature selection approaches, namely the Genetic Algorithm (GA) and Principal Component Analysis (PCA), to select more informative features for an efficient classification process. GA is an evolutionary algorithm that improves the selection process over time.