NOVEL RISK ASSESSMENT FRAMEWORK FOR SMART GRID

A survey on cyber attacks against nonlinear state estimation in power systems of ubiquitous cities

As a critical infrastructure, the electric power grid is vulnerable with respect to malicious physical and cyber attacks. Indeed, the importance of physical and cyber security for smart grids has been highlighted by recent intrusion incidents worldwide. Various types of sensors, instrumentation, information and communications technology (ICT), computational methodologies, and security controls have been developed to enhance the physical and cyber security of the smart grid. In this paper, critical vulnerabilities of a smart grid that can be exploited for physical and cyber intrusions are discussed. A comprehensive survey is conducted on the state‐of‐the‐art research to enhance the physical and cyber security in a smart grid environment. Furthermore, the interdependency of physical and cyber security is illustrated with an intrusion scenario. The emphasis is on the physical and cyber security of power substations. Computer simulation results for a case study are presented. WIREs Energy Environ 2016, 5:519–542. doi: 10.1002/wene.202This article is categorized under: Energy Infrastructure > Systems and Infrastructure

Developments in information and communication technologies (ICT) achieved new trends such as smart grid (SG). Innovations in ICT bring challenges to our security and expectations of privacy. ICT has such a big attack surface that possess increased risks from cyber-attacks in smart grid. Actually, as a critical infrastructure, the smart grid is more sensitive to cyber-attacks, as monitoring and control of smart grid can be realized over standard internet-based protocols. Therefore, an attacker may achieve financial cost to the service and create loss of electric resources, through breaching the real-time balance between energy production and consumption, by controlling data produced by the smart entities. Therefore, in this study, bagging ensemble classifier is evaluated for intrusion detection in a smart grid with a benchmark data. Experimental results have revealed the practicality of Bagging ensemble classifier by realizing a better performance for the cyber security threats in a smart grid environment. The results indicated that the suggested Bagging ensemble classifier with REP Tree achieved a total classification accuracy of 99.94%.

We can observe development of electrical power systems related to ICT (Information and Communication Technologies) throughout the recent years. These technologies are used to improve management of technological processes, reading control and measurement data from remote devices, as well as to improve operation and efficiency of the entire electrical power system. Currently, ICT technologies make it possible for energy distribution and billing systems to evolve into Smart Grids. The article goes into detail discussing methods of metering and automation systems' commu-nication, synchronization techniques and the role of current time in ensuring high efficiency of the system and digital security of electrical power systems.

Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes with an inevitable side effect that makes the power system more vulnerable to cyber attacks. In this paper, we propose a self-supervised learning-based framework to detect and identify various types of cyber attacks. Different from existing approaches, the proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild which are easily accessible. Specifically, the proposed framework adopts the BERT model from the natural language processing domain and learns generalizable and effective representations from the unlabeled sensing data, which capture the distinctive patterns of different attacks. Using the learned representations, together with a very small amount of labeled data, we can train a task-specific classifier to detect various types of cyber attacks. Experiment results in a 3-area power grid system with 37 buses demonstrate the superior performance of our framework over existing approaches, especially when a very limited amount of labeled data are available. We believe such a framework can be easily adopted to detect a variety of cyber attacks in other power grid scenarios.

In recent years, there has been a tremendous increase in the application of information and communication technologies (ICTs) in critical infrastructures, such as energy grids and communication infrastructures. Smart grids, smart cities, including novel power and energy systems, etc. are current ICT developments that are becoming more complex by the day. It is foreseeable that the future of power and energy systems will be different from today's situation due to much more decentralization, enhanced communication, monitoring capabilities, big data, and cyber-security threat. Although various machine learning and deep learning models have been applied to the related tasks, there is still a lack of powerful techniques to equip power and energy systems for a more complex future. Recent advances in artificial intelligence techniques have led Google to develop a new technique, federated deep learning. The use of this new technique would have many advantages, such as the ability to present a global server, cover difficulties in data collection, privacy preservation, high-performance computing, and practical data adaptation. This technique has been used in various fields recently and has attracted much attention and interest. However, it has rarely been applied in power and energy systems. In this chapter, we propose to introduce the federated algorithm in detail, present its most interesting features, and summarize recent applications of this technique in power and energy systems.

Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack events than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning.

Guest Editorial: Situational awareness of integrated energy systems

Cyber attack models for smart grid environments

Smart Grid is one of the critical technologies that provide essential services to sustain social and economic developments. There are various cyber attacks on the Smart Grid system in recent years, which resulted in various negative repercussions. Therefore, understanding the characteristics and evaluating the consequences of an attack on the Smart Grid system is essential. The combination of Graphical Security Model (GrSM), including Attack Tree (AT) and Attack Graph (AG), and the Common Vulnerability Score System (CVSS) is a potential technology to analyze attack on Smart Grid system. However, there are a few research works about Smart Grid attack analysis using GrSM and CVSS. In this research, we first conduct a comprehensive study of the existing research on attack analysis using GrSM and CVSS, ranging from (1) Traditional Networks, (2) Emerging Technologies, to (3) Smart Grid. We indicate that the framework for automating security analysis of the Internet of Things is a promising direction for Smart Grid attack analysis using GrSM and CVSS. The framework has been applied to assess security of the Smart Grid system. A case study using the PNNL Taxonomy Feeders R4-12.47-2 and Smart Grid network model with gateways was conducted to validate the utilized framework. Our research is enriched by capturing all potential attack paths and calculating values of selected security metrics during the vulnerability analysis process. Furthermore, AG can be generated automatically. The research can potentially be utilized in Smart Grid cybersecurity training.KeywordsSmart GridGraphical Security Model (GrSM)Common Vulnerability Score System (CVSS)Attack analysisAttack treeAttack graph

Smart Grid is a modern electric power infrastructure that incorporates elements of traditional power systems and information and communication technology (ICT) with the aim to improve the reliability, efficiency and safety as crucial requirements of critical infrastructure systems. Relying on the ICT, Smart Grid exposes electric power systems to new security issues and vulnerabilities over the network. Therefore, the security is becoming an increasing concern at both physical and information levels. Access controls are one of the most important aspects of information security. Role-based access control (RBAC) model is widely used in complex systems that are characterized by many participants. However, the existing models are not suitable for critical infrastructure systems with specialized features, such as a high number of devices dispersed over the vast geographical regions in “an Internetlike distributed environment”. In this paper, access control mechanism optimized for Smart Grid systems is developed considering the regional division and concept of areas of responsibility (AOR). To this end, the standardized RBAC model will be extended to provide an efficient and consistent policy with greater level of granularity from the aspect of resource protection. The proposed RBACAOR model is formally described and then applied to the smart grid environment.

Huge amounts of data are coming from the electrical grid, through smart meters, smart inverters, and Supervisory Control and Data Acquisition (SCADA) protocols. This makes emerging power systems, and especially distribution grids, heavily dependent on real-time data from the field, as well as on the IT infrastructure for observation and control of field devices. As a consequence, emerging electrical systems are becoming fully digitized, cyber-physical systems with all the accompanying advantages and challenges. Such a power system is called a “Smart Grid”. As all cyber-physical systems, Smart Grids are heavily dependent on Information and Communication Technology (ICT) infrastructure. Therefore, they are highly vulnerable to attacks that can compromise uninterrupted data flow, such as denial of service attacks and interruptions due to natural disasters, cataclysmic events, or wars, as well as due to malicious cyber-attacks. In this paper, we summarize the state-of-the-art in the cyberphysical security of distributed energy resources (DERs). We explore pain points to which monitoring and control of DERs in emerging electrical systems are exposed due to malicious cyberattacks. We continue with analyzing the accompanying consequences to the safety and reliability of emerging Smart Grids. Finally, we present our idea of Digital Twin technology as a tool for assisting distribution system operators and grid engineers, to detect, attenuate, and prevent malicious cyberattacks in DERs.

Modern critical infrastructures such as Smart Grids (SGs) rely heavily on Information and Communication Technology (ICT) systems to monitor and control operations and states within large-scale facilities. The potential offered by SGs includes an effective integration of renewables, a demand-response action and a dynamic pricing system. The increasing use of ICT for the communication infrastructure of modern power systems offers advantages but can give rise to cyber attacks that compromise the security of the SG. To deal efficiently with the security concerns of SGs, a survey of the different attacks that consider the physical as well as the cyber characteristics of modern power grids is required. In the present paper, first the specific differences between SGs with respect to both Information Technology (IT) systems and conventional energy grids are discussed. Thereafter, the specific security requirements of SGs are presented in order to raise awareness of the new security challenges. Finally, a new classification of cyber attacks, based on the architecture of the SG, is proposed and details for each category are provided. The new classification is distinguished by its focus on the cyber-physical security of the SG in particular, which gives a comprehensive overview of the different threats. Thus, this new classification forms the necessary knowledge-basis for the design of respective countermeasures.

The notion of a “smart grid” results from the pressing need to reduce greenhouse gas emissions and replace the conventional power system with cutting-edge ICT infrastructures. A transparent technology with a two-way energy supply system is the smart grid. This state-of-the-art technology, an intelligent network, enables utilities to effectively and efficiently manage energy transmission to end users and give energy consumers the most control over their energy use. The fundamental goal of a smart grid is to ensure energy efficiency while involving end users in the power networks through smart meters and smart houses. Smart Grid uses information and communication technologies to collect real-time data and speed up decision-making. This study aims to provide light on the Smart Grid idea, its historical context and driving forces, the relationship between smart meters and smart grids, the current research agenda in this field, and case studies of its integration in various power systems throughout the world. Although there is no single definition for the term “smart grid,” this study will help describe the enabling technologies, research trends, and case studies of smart grid integration in power systems.

The smart grid (SG) is an emerging technology. To overcome the limitations of traditional power grid systems, it is an optimistic alternative. Smart grid includes green energy resources and enhances the reliability of power generation, transmission, as well as distribution. This transformation involves a bidirectional communication network that monitors the power flow and stores the data at each level. Moreover, as numerous information data and communication devices are involved in the smart grid, it is a prime target for cyber attacks. Targeting the smart grid can lead to disclosure of private data, supply chain failures, and it can also lead to blackouts. Therefore, cyber security becomes a major concern of all power companies around the world. This paper talks about the basic overview of SG, its security objectives, followed by the existing vulnerabilities in the SG network, attack types, some major attacks on the power grid, and highlight some recent solutions.KeywordsSmart grid (SG)VulnerabilitiesCyber securitySmart grid security