The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.

Unmanned aerial vehicles (UAVs), e.g., drones, have become crucial assets in the military’s fleet of vehicles. UAVs can provide limited bandwidth for tactical communications and can act as relays over battlefields. Modern drones provide much higher bandwidth than their legacy predecessors with dynamic antenna capabilities that would be useful in communicating around obstacles, such as urban corridors formed by rows of tall buildings that limit terrestrial lines of sight and attenuate high frequencies. While it is still more likely that one UAV is used for this purpose, a well-managed cluster of UAVs could increase the functionality of the entire terrestrial-drone network. Software-defined networking (SDN) is recognized as an effective way to manage distributed wireless networks. In our previous research, we used a software-defined UAV (SD-UAV) network to offer organized and secure communication resources and relaying capabilities to soldiers, military vehicles, and assets on the ground in a signal-challenged urban environment. Additionally, we developed a framework for detecting common multiple types of cyberattacks (black hole, gray hole, and jamming) using the Light Gradient Boosting machine learning algorithm. However, zero-day attacks are newly developed cyberattacks that can cause significant harm to the functionality of a SD-UAV network. Due to the novelty of these attacks, there is a lack of data samples for ML model training and testing. In order to tackle this problem, this paper provides a predictive queuing analysis of a SD-UAV network. To our knowledge, no previous work has proposed using queuing analysis to predict network performance statistics of SD-UAV networks for the purpose of enhancing security against zero-day cyberattacks. This analysis allows the network operator to calculate the expected theoretical values of the average interarrival time, transmission delay, and packet count of each SD-UAV in a swarm under normal, non-attack operating conditions. This allows the network operator to establish a normal baseline for ML training in order to compare and detect zero-day attacks by detecting any malicious or abnormal SD-UAV network behavior. In this study, we conduct an analysis, simulation, and discussion of the results of our predictive queuing analysis. Our simulation results confirm the accuracy of our predictive queuing analysis we obtained for the average interarrival times, transmission delay, and packet count for the SD-UAVs in the swarm. Our predictive queuing analysis enables the network operator to predict network performance statistics of the SD-UAVs for the purpose of enhancing security against zero-day cyberattacks.

SATCOM is crucial for tactical networks, particularly submarines with sporadic communication requirements. Emerging SATCOM technologies, such as low-earth-orbit (LEO) satellite networks, provide lower latency, greater data reliability, and higher throughput than long-distance geostationary (GEO) satellites. Software-defined networking (SDN) has been introduced to SATCOM networks due to its ability to enhance management while strengthening network control and security. In our previous work, we proposed a SD-LEO constellation for naval submarine communication networks, as well as an extreme gradient boosting (XGBoost) machine-learning (ML) approach for classifying denial-of-service attacks against the constellation. Nevertheless, zero-day attacks have the potential to cause major damage to the SATCOM network, particularly the controller architecture, due to the scarcity of data for training and testing ML models due to their novelty. This study tackles this challenge by employing a predictive queuing analysis of the SD-SATCOM controller design to rapidly generate ML training data for zero-day attack detection. In addition, we redesign our singular controller architecture to a decentralized controller architecture to eliminate singular points of failure. To our knowledge, no prior research has investigated using queuing analysis to predict SD-SATCOM controller architecture network performance for ML training to prevent zero-day attacks. Our queuing analysis accelerates the training of ML models and enhances data adaptability, enabling network operators to defend against zero-day attacks without precollected data. We utilized the CatBoost algorithm to train a multi-output regression model to predict network performance statistics. Our method successfully identified and classified normal, non-attack samples and zero-day cyberattacks with over 94% accuracy, precision, recall, and f1-scores.

Smart grids are replacing conventional power grids due to rising electricity use, failing infrastructure, and reliability problems. Two-way communication, demand-side administration, and real-time pricing make smart grids (SGs) dependent on its communication system. Manual network administration slows down SG communication. SG networks additionally utilize hardware and software from several vendors, allowing devices to communicate. Software-defined SGs (SD-SG) use software-defined networking (SDN) to monitor and regulate SG global communication networks to address these concerns. SDN separates the data plane (routers and switches) from the control plane (routing logic) and centralizes control into the SDN controller. This helps network operators manage visibility, control, and security. These benefits have made SDN popular in SG architectural and security studies. But because SD-SGs are vulnerable to cyberattacks, there are concerns about the security of these SD-SG networks. Cybercriminals can attack software-defined communication networks, affecting the power grid. Unauthorized access can be used to intercept messages and introduce false data into system measurements, flood communication channels with fraudulent data packets, or target controllers, a potential single point of failure, to cripple SDN networks. Current research reflects this paradigm as defense and security against such attacks have developed and evolved. There is a need for a current study that provides a more detailed analysis and description of SD-SG network security dangers and countermeasures, as well as future research needs and developing threats for the sector. To fill this void, this survey is presented.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.