# AI-Enabled Compressive Spectrum Classification for Wideband Radios
## 1 Introduction
In contrast, the authors of [24; 25] used a different method to compute SCF directly from sub-Nyquist samples by using sparsity in both cyclic and angular frequency domains. Although CR technology was initially introduced as a solution for OSA, due to its intelligent nature it has also raised serious concerns about communications security. Due to its sensing capabilities, CR node can monitor activities over a wide range of the spectrum, and, therefore, is able to disrupt the ongoing communications by launching smart jamming attacks [26; 27]. Further, it can imitate the legitimate user and conduct the primary user emulation (PUE) attacks [28]. Because the radio communications channel is unprotected, therefore, the physical layer (PHY-layer) is particularly vulnerable to radio frequency (RF) jamming assaults. As a result, the need for communications security in the Cyber-Physical Systems (CPSs) and Internet-of-Things (IoT) frameworks [29], has forced the development of a suitable paradigm. The SHIELD project, which incorporates approaches and algorithms for creating secure embedded communication systems [29], is a significant progress in this direction. TVWS [30] technology has also addressed the issue of detecting unauthorized users. Our article focuses on stealthy jamming attacks; such jammers embed the spectrum sensing capability and therefore, only initiate transmission once energy is detected in a frequency band and terminate its transmission once the targeted signal stops transmission. Hence, such jamming attacks are hard to spot using conventional detection methods on the network layer. Most of the previous studies that explore RF jamming assumed tone-jamming attacks [31; 32]. However, in more recent works [33; 34], authors showed that modulated pulsed jamming attacks could be more effective than tone jamming in power constrained environments; therefore, they are studied in this work. Moreover, a stealthy jammer embeds a feature detector (FD) and therefore extracts features of the targeted signals and identifies their modulation schemes, hence using the best jamming scheme to jam the targeted signal. A reliable detection technique is required to design an anti-jamming system to mitigate stealthy jamming attacks. Therefore, this work introduces a robust and energy-efficient algorithm to not only detect stealthy jamming attacks but also classify legitimate modulated signals. The key contributions of the article are as follows: (i) using CS, CFD, and artificial neural networks, classify the multiple available NB signals in the WB spectrum into their corresponding modulation schemes; and (ii) detect stealthy modulated pulse jamming attacks using the previously trained artificial neural network. Our newly proposed algorithm can be explained as follows. The CR node scans a WB spectrum made up of \(M\) sub-bands. The WB is sparsely occupied with narrow-band (NB) communications, which could be either licit or blocked by the stealthy jammer. Compressed sensing is exploited to ease the requirements of Nyquist rate sampling and the WB spectrum that is sparsely occupied is reconstructed from sub-Nyquist samples. Then each NB signal in WB is sent to CFD which computes the SCF and the corresponding frequency (\(f\)) profile and cycle (\(\alpha\)) frequency profile are used as a feature vector for training the artificial neural network that classifies signals as legitimate communications with a specific modulation type or as jammed by a pulse-modulated stealthy jammer. The algorithm's performance is further evaluated with an independent set of signals that are obtained at different compression ratios and SNRs from the one used in the previous stage. The algorithm has been shown to achieve high accuracy for both jammer detection and signals classification at low compression rates and SNRs compared to techniques discussed in the literature. The rest of the article is structured as follows. Section 2 details the materials and methods.