Emerging Smart Technologies for Critical Infrastructure

A satellite communication system, as a typical example of the Internet of things, is a smart critical infrastructure and has become an essential component used in various services such as finances, communications, ground and air-borne navigation, utilities, power grid distribution, emergency services, agriculture, banking, and many other critical industries. In recent times, satellite communication systems have become a target for cyber-attack. In this chapter, we review satellite infrastructure and the existing cybersecurity frameworks applied in smart critical infrastructure. We identified three main cybersecurity properties for satellite smart critical infrastructure, which are real-time analysis, mitigation mechanism, and low computational overhead. These properties are mapped against existing cybersecurity frameworks applied in smart critical infrastructure. The result indicated that the existing cybersecurity frameworks are either inapplicable, incompatible, or inadequate to address the cyber-attacks in satellite smart critical infrastructure. In addition, we identify a combination of mechanisms such as runtime verification and digital twin technology to address the satellite smart critical infrastructure cybersecurity. Finally, we discuss a review of the mechanisms and their applications along with our future work.

As a trustworthy, decentralized ledging technology, blockchain has gained significant attention in the industry. In the last a few years, research and development work has been conducted in underpinning the capability of blockchain to support the secure, reliable and efficient operation of power and energy systems. This chapter provides a review for applications of blockchain in different operational aspects of smart grids. It introduces the basic concepts related to blockchain and smart grids and provides a taxonomy for blockchain-supported smart grid applications. It then reviews the recently representative work in each application area. The chapter is expected to provide a reference for researchers and engineers in the related fields.

Organisations are increasingly looking for ways to further utilise big data and the benefits that come with this. Previously, this role has been taken by traditional machine learning algorithms. However, these have drawbacks such as computation cost and privacy issues. Federated machine learning (FML) seeks to remedy the downfalls of traditional machine learning. Client selection is one way in which to further improve FML, as which clients that are chosen, and how they operate are a core part of its operation. This paper proposes a potential better way to operate a client selection framework, after reviewing the current literature within academia.

Due to the increasing number of threats against Cyber Physical System (CPS) networks, security monitoring in these networks is challenging. Machine learning methods have been widely used to analyse network data and detect intrusions automatically. However, these automated intrusion detection systems (IDSs) are black boxes, and there is no explanation for their decision. Therefore, explainable machine learning techniques can be used to explain the reasons behind the decision made by machine learning-based IDSs. However, there is no sufficient study on explainable IDSs in CPS networks. The other challenge in CPS networks is the growing volume of data. A NetFlow-based analysis is a scalable method suitable for a high volume of data. However, the efficiency of such a method in CPS networks has not been sufficiently investigated. In this chapter, we address these challenges by proposing an explainable NetFlow-based IDS (X-NFIDS) for CPS networks. The Internet of Things (IoT) environment is used as an example of CPS networks. To demonstrate the feasibility of our approach, we perform some preliminary studies of the proposed method using two NetFlow datasets for IoT.

In material science, understanding structure, property, performance, processing, characterization and those relationships is a main concern for experimental scientists. Also, discovering new materials to address some of crucial global challenges such as health and medicine, food and water security, climate, etc. raises other important issues for material scientists in terms of cost and time consuming. In this regard, combining data science and machine learning knowledge with the experts of material scientists can help us providing better guidance to solve these questions: What material to make? how to make them? And how to recognize their properties? This chapter is conducted into two separated parts applicable by machine learning models: (1) material science and (2) problem solving. First section of this chapter aims to introduce us with some progressing in this area. This section specifically considers automatic solutions for material science known as material informatics in order to prevent information security threats particularly information integrity threats that is probable in national security organizations. The second section attends to two useful graph-based models in problem solving. Graph-based models have been playing the most important role in unsolved problems such as computer vision, engineering, security and medicine for many years. Nowadays, there have been significant efforts in producing mature and improved graph-based algorithms. The aim of this research is to introduce two prevalent graph-based methods, namely, graph-cut models (deterministic) and a unified graphical model (probabilistic) in a simple word.

The engineering behind the technology that powers Bitcoin, known as Blockchain, has gained attention as a potential software solution for various industrial applications. The capability of revolutionising digital transactions brought significant interest in this technology and evolved greatly in the past decade. However, the development of applications beyond cryptocurrency has not yet kept pace. In this chapter, we will examine the basic design principles of blockchain technology using Bitcoin’s architecture as a foundation and understand the rationale behind its design and the limitation of scalability.

The Internet of Things (IoT) smart grid enables many benefits to both customers and energy generators, such as improved outage visibility, billing, and cost reduction. It allows more efficient energy use through improved access to real-time data that supports customers in reducing their energy usage and improving environmental outcomes. Integrating IoT-based data networks into the grid brings these benefits and many more. However, security and performance challenges are introduced. With the plethora of current and emerging technologies, suitable technologies must be used in each network segment that provide a sufficient level of network performance. With the introduction of data networks to the grid, we must also consider what additional threats are introduced regarding network security. This work provides essential background information on the residential smart grid. Security risks and attacks that threaten the IoT smart grid are then identified. A discussion on security challenges and future research directions are presented. A review and discussion of relevant modern IoT transmission technologies covering their benefits, key performance metrics, and their appropriate place within the IoT-based smart grid is then presented.