# Detecting Jamming in Smart Grid Communications via Deep Learning
## 2 Related Work
[33] used an ML-based approach to detect jamming in a wireless IoT network by collecting the signal strength indication received from five anchor nodes at minimal communication cost. In the context of anomaly detection in an Industrial Control System (ICS), the authors of [34] used autoencoders to encode normal profile data and then detect anomalies based on the reconstruction error. [PERSON] et al. [35] devised a genetic-algorithm-based Cumulative Sum (CUSUM) approach to the real-time detection of jamming attacks in distributed and centralized Cyber-Physical Systems (CPSs). **Radio Frequency Fingerprinting:** Radio Frequency Fingerprinting (RFF) leverages the unique hardware imperfections inherent in radio frequency front ends to enable device authentication. RFF-based solutions have emerged as a highly promising alternative to traditional cryptographic and localization-based methods for device identification and authentication [36]. Beyond authentication, RFF techniques have found applications in intrusion detection systems, device tracking, autonomous vehicles, and unmanned aerial vehicles (UAVs) [37]. [PERSON] et al. [38] employed Multiple Discriminant Analysis and Maximum Likelihood (MDA/ML) classifiers to differentiate among six PLC devices, achieving an intrusion detection accuracy of 90%. Their approach utilized physical-layer features extracted from X10 protocol-based devices. In another study, [PERSON] et al. [39] demonstrated that augmenting the physical layer with Constellation-Based Distinct Native Attribute (CB-DNA) features derived from unintentional Ethernet cable emissions can significantly enhance network security, achieving Rogue Reject Rates between 85.2% and 93.1% at SNR levels above 26.0 dB. **Research Gap in PLC Jamming Detection:** As shown in Table 1, the existing jamming detection approaches span various domains, and none of them address the task of detecting jamming attacks to PLC networks at the physical layer, particularly under low-BER scenarios. Some of the proposed solutions already exploit physical-layer information but consider different communication media, e.g., wireless networks [19; 30; 32]. Other solutions consider the SG scenario but resort to higher-layer metrics, e.g., PDR, thus being able to detect the presence of the jammer when it already affects the quality of the link [35]. Finally, as highlighted in Table 1, we stress that our analysis is the first one to (i) consider different assumptions at the receiver side, i.e., the receiver might or might not be exposed before (during training) to the presence of the jammer, (ii) estimate the performance of the jamming detection as a function of the distance between the jammer and the receiver, and, finally, (iii) evaluate the performance of the detection as a function of the jamming power. In our previous work [11], we formulated jamming detection as a classification problem under the assumption that the model has been trained on jamming signals; i.e., the receiver is aware of the shape of the jammed signals and, therefore, trains the model on both jammed and non-jammed samples. In real-world scenarios, this might be difficult. Therefore, in this work, we assume that we have access to only clear signals transmitted and received in the presence of noise typical of the PLC medium. Therefore, we only train our model on clear samples (non-jammed signals) and compare the performance with the previously proposed solution. To the best of our knowledge, no previous work focused on the detection of jamming in PLC infrastructures under a low-BER regime.