This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.

World energy outlook

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.

/pdf/deep-learning-for-cyber-security-intrusion-detection-gwyxxf1kkj.pdf

Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.

Survey on SDN based network intrusion detection system using machine learning approaches

Integration of renewable resources and increased growth in energy consumption has created new challenges for the traditional electrical network. To adhere to these challenges, Internet of Everything (IoE) has transformed the existing power grid into a modernized electrical network called Smart Grid. An integral part of this transformation is the Advanced Metering Infrastructure (AMI), which enables two-way communication for flow of information consisting of energy consumption, outages, and electricity rates between smart meters and the utilities. These enhanced AMI features and privileges have resulted in a larger surface for cyber-attack, enabling remote exploitation of these smart devices without any physical access. Therefore, consumer privacy and security has become a critical issue due to the interconnection of different smart devices in various communication networks and the information they carry. In this paper, we present a comprehensive survey of privacy related research in the IoE enabled smart grid environment. The survey presents a detailed analysis of privacy problems and their corresponding solutions in AMI. Our goal is to provide an in-depth understanding of the smart grid and shed light on future research directions.

A survey of privacy preserving schemes in IoE enabled Smart Grid Advanced Metering Infrastructure

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

https://www.mdpi.com/1424-8220/19/23/5236/pdf

Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm.

The consensus mechanism is a core component of Blockchain technology, allowing thousands of nodes to agree on a single and consistent view of the Blockchain. A carefully selected consensus mechanism can provide attributes such as fault tolerance and immutability to an application. The Internet of Things (IoT) is a use case that can take advantage of these unique Blockchain properties. IoT devices are commonly implemented in sensitive domains such as health, smart cities, and supply chains. Resilience and data integrity are important for these domains, as failures and malicious data tampering could be detrimental to the systems that rely on these IoT devices. Additionally, Blockchains are well suited for decentralised networks and networks with high churn rates. A difficulty involved with applying Blockchain technology to the IoT is the lack of computational resources. This means that traditional consensus mechanisms like Proof of Work (PoW) are unsuitable. In this paper, we will compare several popular consensus mechanisms using a set of criteria, with the aim of understanding which consensus mechanisms are suitable for deployment in the IoT, and what trade-offs are required. We show that there are opportunities for both PoW and PoS to be implemented in the IoT, with purpose-made IoT consensus mechanisms like PoSCS and Microchain. Our analysis shows that Microchain and PoSCS have characteristics that are well suited for IoT consensus.

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A Comparative Study of Consensus Mechanisms in Blockchain for IoT Networks

Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.

Rabei Alhadad

Papers

Survey on SDN based network intrusion detection system using machine learning approaches

A survey of privacy preserving schemes in IoE enabled Smart Grid Advanced Metering Infrastructure

Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm.

A Comparative Study of Consensus Mechanisms in Blockchain for IoT Networks

Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network.