# Detecting Jamming in Smart Grid Communications via Deep Learning
###### Abstract  
Power-Line Communication (PLC) allows data transmission through existing power lines, thus avoiding the expensive deployment of ad hoc network infrastructures. However, power line networks remain vastly unattended, which allows tampering by malicious actors. In fact, an attacker can easily inject a malicious signal (jamming) with the aim of disrupting ongoing communications. In this paper, we propose a new solution to detect jamming attacks before they significantly affect the quality of the communication link, thus allowing the detection of a jammer (geographically) far away from a receiver. We consider two scenarios as a function of the receiver's ability to know in advance the impact of the jammer on the received signal. In the first scenario (jamming-aware), we leverage a classifier based on a Convolutional Neural Network, which has been trained on both jammed and non-jammed signals. In the second scenario (jamming-unaware), we consider a one-class classifier based on autoencoders, allowing us to address the challenge of jamming detection as a classical anomaly detection problem. Our proposed solution can detect jamming attacks on PLC networks with an accuracy greater than 99% even when the jammer is 68 m away from the receiver while requiring training only on traffic acquired during the regular operation of the target PLC network. p 2025 14 May  
devices. Industrial applications are also leveraging PLC for reliable and efficient communication in automation systems. Regionally, Europe and North America hold a significant market share, supported by investments in grid modernization and automation, while the Asia-Pacific region is emerging as a key growth area due to large-scale smart meter deployments and smart city initiatives [2]. As a matter of fact, PLC offers a reliable means of transmitting data through electrical cables since the electrical power infrastructure is already in place, thus enabling PLC technology to provide an efficient and cost-effective solution to communicate in various industrial, commercial, and residential applications [3]. Furthermore, PLC eases the deployment of many SG functions by offering real-time monitoring, load management, and fault detection within the power grid [4]. As described by the authors in [5], PLC is usually classified based on the spectrum it operates in. Ultra-Narrowband PLC (UNB-PLC) operates within a very narrow frequency band, typically below 3 kHz. Its key advantage lies in the ability to filter out significant noise, ensuring reliable communication even in high-noise environments. UNB-PLC transmits data using a single channel and, unlike its counterparts, provides data rates of tens of bits per second, relegating its applications to command-and-control scenarios. Broadband PLC (BB-PLC) operates at higher frequencies (typically between 2 MHz and 250 MHz), achieving high data rates (hundreds of Mbps). Therefore, BB-PLC deployments are more suitable for multimedia streaming and broadband internet access. Finally, an intermediate option is Narrowband PLC (NB-PLC), which operates at lower frequencies (3 kHz to 500 kHz) and is currently used in applications such as smart meters and SG management. In the context of SG, integrating IoT devices has enhanced capabilities, such as disaster recovery, self-healing, warning systems, and overall reliability [6]. Smart buildings, homes, agriculture, and industrial systems are equipped with IoT sensors to monitor the environment and make intelligent decisions. The digital PLC modem, connected with IoT devices, facilitates data exchange between power utilities and consumers, enabling necessary actions [7]. Despite their many advantages, PLC technologies also face challenges specific to their physical medium, including high levels of background noise. The challenge stems from the inherent properties of power cables, which are designed primarily for electricity distribution rather than efficient data transmission.