Internet of Things on Power Line Communications: An Experimental Performance Analysis

The giant information exchange enabled by the Internet of Things (IoT) paradigm, i.e. by a "network of networks" of smart and connected devices, will likely exploit electrical lines as a ready-to-use infrastructure. Power Line Communications (PLC) have received a significant attention in the last decade, as electrical lines are not used as simple energy supply media, but as information carriers. Among the different aspects of PLC-based architectures, an interesting and important analysis have to be reserved to security aspects that should be adopted in similar infrastructures, having that they are crucial to deliver trustworthy and reliable systems and, hence, to support users relying on available services, especially in case in which they should be inherently secure at the physical level (e.g. against unauthorised signal removal/interruption and eavesdropping, since they are difficult and dangerous). Motivated by the relevant impact of PLC on IoT, in this chapter we investigate experimentally the performance of IoT systems on PLC in indoor environments, considering a vendor-provided application tool and a self-developed Java library. The experimental tests are carried out on both cold and hot electrical lines, evaluating both fixed-size and variable-length power lines. Our results show that IoT-oriented PLC can reach a throughput of 8 kbps on a 300-m cold line and of 6 kbps on a 300-m hot line. Further experimental efforts will be oriented to performance analyses in presence of the adoption of security measures

How machine learning can support cyberattack detection in smart grids

This chapter addresses the application of machine learning algorithms to detect attacks against smart grids. Smart grids are the result of a long process of transformation that power systems have been through, relying on Information and Communication Technology (ICT) to improve their monitoring and control. Although an objective of this convergence of power systems and ICT is to increase their reliability, the dependency on information technology has brought new cybersecurity vulnerabilities to this scenario. Therefore, developing new cybersecurity measures for smart grids is a key factor in their success. One of these measures is attack detection, which allows the timely mitigation of attacks with the aim of limiting possible damages to the targets. As machine learning algorithms have been widely applied as powerful tools to support the design of cybersecurity solutions in multiple areas, they also have huge potential for addressing the new challenges that smart grids pose. With this as the foundational perspective, this study starts by presenting an overview of smart grids, followed by possible attacks. After this discussion, we examine the background concepts for attack detection and machine learning. Then, we discuss the existing solutions, showing in detail how they address the particularities of smart grids and their attack types using machine learning algorithms. This is supplemented by a discussion of the open issues in the use of machine learning for smart grid attack detection, followed by some future research directions