# Detecting Jamming in Smart Grid Communications via Deep Learning
## 2 Related Work  
PLC has paramount importance for communication in power systems, providing a cost-effective and reliable communication solution for SG applications. The possibility to use PLC for indoor and outdoor networks allows it to be used in a wider range of applications. However, its broadcast nature and ease of access to the power grid expose legitimate communicating devices, making them susceptible to malicious attacks and eavesdropping. PLC networks face unique security challenges due to their dual role in power delivery and communication. These networks are particularly vulnerable to physical-layer attacks due to (i) the widespread accessibility of the power infrastructure, (ii) the difficulty in distinguishing between natural noise and malicious interference [12], (iii) the resource constraints of PLC devices [13], and (iv) the critical nature of SG communications [14]. As SGs usually include resource-constrained devices, traditional cryptographic systems may not be a feasible solution for all devices, thus creating an opportunity for physical-layer security solutions. The topic is not novel for PLC and has undergone thorough examination in numerous research papers [15; 16; 17; 18]. **Jamming Detection:** Jamming detection has been extensively studied in wireless and other networked domains. [PERSON] et al. [19] proposed BloodHound+, a technique for early jamming detection on drones leveraging autoencoders. This early detection allows the user to take the necessary actions before the communication link drops. In SG environments, DoS attacks typically manifest as either flooding or jamming. As for flooding attacks, [PERSON] et al. [20] and [PERSON] et al. [21] focused on transport-layer vulnerabilities, while [PERSON] et al. [22] and [PERSON] et al. [23] addressed network-layer attacks and application-layer attacks, respectively. In contrast, our work focuses on physical-layer data, specifically addressing jamming attacks that manipulate the communication medium itself rather than affecting quality metrics associated with higher protocols. As for jamming attacks in wireless domains, [PERSON] et al. [24] implemented anomaly detection systems, while [PERSON] et al. [25] explored geometric delay mechanisms. Further contributions by [PERSON] [26] and [PERSON] et al. [27] expanded the background knowledge on wireless jamming characteristics. However, our work addresses the fundamentally different challenge of jamming in PLC networks, where the attack surface and signal propagation characteristics differ significantly from wireless environments. [PERSON] et al. [28] used packet delivery ratio (PDR), received signal strength (RSSI), and Start-of-Frame Delimiter (SFD) features to identify and detect jamming using threshold-based statistics. **Machine Learning for Network Security:** [PERSON] et al. [29] proposed the jamming attack detection technique (JADT) to detect jamming for opportunistic networks, which uses PDR and the decryption of acknowledgment sent by the sender; once jamming is detected, it requests the sender to re-transmit the message through a different channel. [PERSON] et al. [30] used an ML-based technique to detect and classify the types of jamming attacks. They validated the results through a simulation study considering reactive, random, and constant jamming interference. Another study [31] used the BER and bad packet ratio and an energy statistic to detect the jamming for the automatic dependent surveillance-broadcast (ADS-B) system. Supervised machine learning algorithms such as support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and decision tree were employed. The ANN outperformed with the given set of features. In [32], the author proposed the detection of jamming considering shallow neural networks aided by dimensionality reduction at the gateway level of the network, considering three different types of jamming signals, namely constant, random, and reactive. [PERSON] et al.