# Detecting Jamming in Smart Grid Communications via Deep Learning
###### Abstract
As the signal propagates through the cable, some of its energy is dissipated as heat due to resistance or reflections [8]. Energy dissipation causes signal attenuation, i.e., a reduction in the signal amplitude, leading to weaker received signals at the destination [9]. Noise from different sources, including Electromagnetic Interference (EMI) from other devices, switching transients, and Radio Frequency (RF) interference, also contribute to signal degradation [10]. At the same time, PLC technologies are vulnerable to traditional cyber-attacks. In this context, Denial of Service (DoS) constitutes a major challenge in PLC. It can be performed through a variety of techniques, including flooding, desynchronization, amplification, false data injection attacks, and jamming. It consists of injecting artificial noise into the communication channel in order to prevent the receiver from detecting the transmitted messages. Jamming is particularly effective since, even if a node detects the attack, reporting the jamming detection event to remote entities is challenging as all communication links should be considered disrupted under a jamming attack. Moreover, distinguishing between a noisy communication line and a jamming attack is particularly challenging due to the high level of noise that affects the PLC channel. This is particularly true for remote network management entities, which cannot easily detect ongoing jamming attacks due to the distance of their monitoring units from the monitored network. Jamming attacks against PLC systems can be detrimental to the overall infrastructure of the target organization as they can lead to disruption of energy services, unpredictable effects on Home Automation Systems (HASs), safety hazards, communication breakdowns, and ultimately negative financial consequences. **Motivation. ** Jamming attacks in PLC networks are particularly concerning due to their ability to degrade communication without easily distinguishable signatures, especially in noisy environments. The existing solutions, inspired by wireless and wired networks, fail to address the unique challenges posed by the PLC medium. The lack of targeted approaches for robust jamming detection leaves critical infrastructures exposed to operational disruptions and safety hazards. Our research aims to address the gap that arises when the Bit Error Rate (BER) \(\approx 0\). By employing machine learning techniques, such as Convolutional Neural Networks (CNNs) and autoencoders, we aim to develop a robust jamming detection system specifically designed for PLC environments. Our solution not only addresses the limitations of the existing methods but also considers realistic deployment scenarios, including different adversarial assumptions and heterogeneous channel conditions. **Contribution. ** In this paper, we introduce a jamming detection technique specifically designed for PLC scenarios under different adversarial assumptions. This paper extends our previous work [11] as per the following points:  
* We consider a brand new scenario that includes \"jamming unawareness\" during the training of the model, and we compare this scenario with the one previously proposed. * We improve our system model by formulating the jamming detection as an anomaly detection problem. * We compare the multi-class classification solution from our previous work with the new one-class classification (based on autoencoders). * We expand our performance analysis considering multiple parameters, such as channel quality (Signal-to-Noise Ratio (SNR)), training metrics, the level of the Relative Jamming Power (RJP), and the distance of the receiver from the jammer. **Roadmap. ** This paper is organized as follows. Section 2 reviews the related work, Section 3 describes the considered scenarios, Section 4 discusses the preliminary notions, Section 5 includes the adversary model and provides the details of our solution, and Section 6 presents the evaluation results. Finally, Section 7 comprises the conclusion.