Biblio

The construction of a strong and smart grid is inseparable from the advancement of the power system, and the effective application of modern communication technologies allows the traditional grid to better transform into the energy Internet. With the advent of 5G, people pay close attention to the application of network slicing, not only as an emerging technology, but also as a new business model. In this article, we consider the delay requirements of certain services in the power grid. First, we analyze the security issues in network slicing and model the 5G core network slicing supply as a mixed integer linear programming problem. On this basis, a heuristic algorithm is proposed. According to the topological properties, resource utilization and delay of the slice nodes, the importance of them is sorted using the VIKOR method. In the slice link configuration stage, the shortest path algorithm is used to obtain the slice link physical path. Considering the delay of the slice link, a strategy for selecting the physical path is proposed. Simulations show that the scheme and algorithm proposed in this paper can achieve a high slice configuration success rate while ensuring the end-to-end delay requirements of the business, and meet the 5G core network slice security requirements.

Electric vehicles (EVs) are expected to be applicable to smart grids because they have large-capacity batteries. It is important that smart grid users be able to estimate surplus battery energy and/or surplus capacity in advance of deploying EVs. We constructed a database, the Energy COnsumption LOG (ECOLOG) Database System, to store vehicle daily logs acquired by smartphones placed in vehicles. The electrical energy consumption is estimated from GPS coordinate data using an EV energy-consumption model. This research specifically examines commuting with a vehicle used for same route every day. We corrected GPS coordinate data by map matching, and input the data to the EV energy consumption model. We regard the remaining battery capacity data acquired by the EV CAN as correct data. Then we evaluate the accuracy of driving energy consumption logs as estimated using the corrected GPS coordinate data.

Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: the first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.

Despite the benefits offered by smart grids, energy producers, distributors and consumers are increasingly concerned about possible security and privacy threats. These threats typically manifest themselves at runtime as new usage scenarios arise and vulnerabilities are discovered. Adaptive security and privacy promise to address these threats by increasing awareness and automating prevention, detection and recovery from security and privacy requirements' failures at runtime by re-configuring system controls and perhaps even changing requirements. This paper discusses the need for adaptive security and privacy in smart grids by presenting some motivating scenarios. We then outline some research issues that arise in engineering adaptive security. We particularly scrutinize published reports by NIST on smart grid security and privacy as the basis for our discussions.

As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow).

This paper proposes an approach based on Bayesian Networks to support cyber security analysts in improving the cyber-security posture of the smart grid. We build a system model that exploits real world context information from both Information and Operational Technology environments in the smart grid, and we use it to demonstrate sample predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the many dependencies involved in the assessment of security threats, and of supporting the security analysts in planning defense and detection mechanisms for energy digital infrastructures.

With the advancement in technology, inclusion of Information and Communication Technology (ICT) in the conventional Electrical Power Grid has become evident. The combination of communication system with physical system makes it cyber-physical system (CPS). Though the advantages of this improvement in technology are numerous, there exist certain issues with the system. Security and privacy concerns of a CPS are a major field and research and the insight of which is content of this paper.

Smart grids, where cyber infrastructure is used to make power distribution more dependable and efficient, are prime examples of modern infrastructure systems. The cyber infrastructure provides monitoring and decision support intended to increase the dependability and efficiency of the system. This comes at the cost of vulnerability to accidental failures and malicious attacks, due to the greater extent of virtual and physical interconnection. Any failure can propagate more quickly and extensively, and as such, the net result could be lowered reliability. In this paper, we describe metrics for assessment of two phases of smart grid operation: the duration before a failure occurs, and the recovery phase after an inevitable failure. The former is characterized by reliability, which we determine based on information about cascading failures. The latter is quantified using resilience, which can in turn facilitate comparison of recovery strategies. We illustrate the application of these metrics to a smart grid based on the IEEE 9-bus test system.

Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like-even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate network views of the text(s), that we then can use for vulnerability mapping, risk assessments and note control point analysis. For proof of concept we draw on NIST conceptual model and analysis of guidelines for smart grid cybersecurity, more than 600 pages of text.

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

Industrial Control System (ICS) transmits control and monitoring data between devices in an industrial environment that includes smart grids, water and gas distribution, or traffic control. Unlike traditional internet communication, ICS traffic is stable, periodical, and with regular communication patterns that can be described using statistical modeling. By observing selected features of ICS transmission, e.g., packet direction and inter-arrival times, we can create a statistical profile of the communication based on distribution of features learned from the normal ICS traffic. This paper demonstrates that using statistical modeling, we can detect various anomalies caused by irregular transmissions, device or link failures, and also cyber attacks like packet injection, scanning, or denial of service (DoS). The paper shows how a statistical model is automatically created from a training dataset. We present two types of statistical profiles: the master-oriented profile for one-to-many communication and the peer-to-peer profile that describes traffic between two ICS devices. The proposed approach is fast and easy to implement as a part of an intrusion detection system (IDS) or an anomaly detection (AD) module. The proof-of-concept is demonstrated on two industrial protocols: IEC 60870-5-104 (aka IEC 104) and IEC 61850 (Goose).

Integration of information technologies with the current power infrastructure promises something further than a smart grid: implementation of smart cities. Power efficient cities will be a significant step toward greener cities and a cleaner environment. However, the extensive use of information technologies in smart cities comes at a cost of reduced privacy. In particular, consumers' power profiles will be accessible by third parties seeking information over consumers' personal habits. In this paper, a methodology for enhancing privacy of electricity consumption patterns is proposed and tested. The proposed method exploits digital connectivity and predictive tools offered via smart grids to morph consumption patterns by grouping consumers via an optimization scheme. To that end, load anticipation, correlation and Theil coefficients are utilized synergistically with genetic algorithms to find an optimal assembly of consumers whose aggregated pattern hides individual consumption features. Results highlight the efficiency of the proposed method in enhancing privacy in the environment of smart cities.

Cyber threats have increased extensively during the last decade, especially in smart grids. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and Advanced Persistent Threat (APT) is almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques can mine data to detect different attack stages of APT. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a balance between risk and benefits.

Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $ε$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.

Various research efforts have focused on the problem of customer privacy protection in the smart grid arising from the large deployment of smart energy meters. In fact, the deployed smart meters distribute accurate profiles of home energy use, which can reflect the consumers' behaviour. This paper proposes a privacy-preserving lattice-based homomorphic aggregation scheme. In this approach, the smart household appliances perform the data aggregation while the smart meter works as relay node. Its role is to authenticate the exchanged messages between the home area network appliances and the related gateway. Security analysis show that our scheme guarantees consumer privacy and messages confidentiality and integrity in addition to its robustness against several attacks. Experimental results demonstrate the efficiency of our proposed approach in terms of communication complexity.

The rapid evolution of the Information and Communications Technology (ICT) services transforms the conventional electrical grid into a new paradigm called Smart Grid (SG). Even though SG brings significant improvements, such as increased reliability and better energy management, it also introduces multiple security challenges. One of the main reasons for this is that SG combines a wide range of heterogeneous technologies, including Internet of Things (IoT) devices as well as Supervisory Control and Data Acquisition (SCADA) systems. The latter are responsible for monitoring and controlling the automatic procedures of energy transmission and distribution. Nevertheless, the presence of these systems introduces multiple vulnerabilities because their protocols do not implement essential security mechanisms such as authentication and access control. In this paper, we focus our attention on the security issues of the IEC 60870-5-104 (IEC-104) protocol, which is widely utilized in the European energy sector. In particular, we provide a SCADA threat model based on a Coloured Petri Net (CPN) and emulate four different types of cyber attacks against IEC-104. Last, we used AlienVault's risk assessment model to evaluate the risk level that each of these cyber attacks introduces to our system to confirm our intuition about their severity.

As opposed to a traditional power grid, a smart grid can help utilities to save energy and therefore reduce the cost of operation. It also increases reliability of the system In smart grids the quality of monitoring and control can be adequately improved by incorporating computing and intelligent communication knowledge. However, this exposes the system to false data injection (FDI) attacks and the system becomes vulnerable to intrusions. Therefore, it is important to detect such false data injection attacks and provide an algorithm for the protection of system against such attacks. In this paper a comparison between three FDI detection methods has been made. An H2 control method has then been proposed to detect and control the false data injection on a 12th order model of a smart grid. Disturbances and uncertainties were added to the system and the results show the system to be fully controllable. This paper shows the implementation of a feedback controller to fully detect and mitigate the false data injection attacks. The controller can be incorporated in real life smart grid operations.

Smart grid employs intelligent transmission and distribution networks for effective and reliable delivery of electricity. It uses fine-grained electrical measurements to attain optimized reliability and stability by sharing these measurements among different entities of energy management systems of the grid. There are many stakeholders like users, phasor measurement units (PMU), and other entities, with changing requirements involved in the sharing of the data. Therefore, data security plays a vital role in the correct functioning of a power grid network. In this paper, we propose an attribute-based encryption (ABE) for secure data sharing in Smart Grid architectures as ABE enables efficient and secure access control. Also, the access policy is obfuscated to preserve privacy. We use Linear Secret Sharing (LSS) Scheme for supporting any monotone access structures, thereby enhancing the expressiveness of access policies. Finally, we also analyze the security, access policy privacy and collusion resistance properties along with efficiency analysis of our cryptosystem.

Rapid development of wireless technologies has driven the evolution of smart grid application. In smart grid, authentication plays an important role for secure communication between smart meter and service provider. Hence, the design of secure authenticated key agreement schemes has received significant attention from researchers. In these schemes, a trusted third party directly participates in key agreement process. Although, this third party is assumed as trusted, however we cannot reject the possibility that being a third party, it can also be malicious. In the existing works, either the established session key is revealed to the agents of a trusted third party, or a trusted third party agent can impersonate the smart meter and establish a valid session key with the service provider, which is likely to cause security vulnerabilities. Therefore, there is a need to design a secure authentication scheme so that only the deserving entities involved in the communication can establish and know the session key. This paper proposes a new secure authenticated key agreement scheme for smart grid considering the fact that the third party can also be malicious. The security of the proposed scheme has been thoroughly evaluated using an adversary model. Correctness of the scheme has been analyzed using the broadly accepted Burrows-Abadi-Needham (BAN) Logic. In addition, the formal security verification of the proposed scheme has been performed using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Results of this simulation confirm that the proposed scheme is safe. Detailed security analysis shows the robustness of the scheme against various known attacks. Moreover, the comparative performance study of the proposed scheme with other relevant schemes is presented to demonstrate its practicality.

Two-factor authentication has been widely used due to the vulnerabilities associated with the traditional password-based authentication. One-Time Password (OTP) plays an important role in authentication protocol. However, a variety of security problems have been challenging the security of OTP, and improvements are introduced to solve it. This paper reviews several schemes to implement and modify the OTP, a comparison among the popular OTP algorithms is presented. A smart grid architecture with edge computing is shown. The authentication techniques in the smart grid are analyzed.

A smart grid is a fully automated power electricity network, which operates, protects and controls all its physical environments of power electricity infrastructure being able to supply energy in an efficient and reliable way. As the importance of cyber-physical system (CPS) security is growing, various vulnerability analysis methodologies for general systems have been suggested, whereas there has been few practical research targeting the smart grid infrastructure. In this paper, we highlight the significance of security vulnerability analysis in the smart grid environment. Then we introduce various automated vulnerability analysis techniques from executable files. In our approach, we propose a novel binary-based vulnerability discovery method for AMI and EV charging system to automatically extract security-related features from the embedded software. Finally, we present the test result of vulnerability discovery applied for AMI and EV charging system in Korean smart grid environment.

Due to geographical locations of Indonesia, some technology such as hydro and solar photovoltaics are very attractive to be used and developed. Distribution Energy Resources (DER) is the appropriate schemes implemented to achieve optimal operation respecting the location and capacity of the plant. The Gorontalo sub-system network was chosen as a case study considering both of micro-hydro and PV as contributed to supply the grid. The needs of a smart electrical system are required to improve reliability, power quality, and adaptation to any circumstances during DER application. While the topology was changing over time, intermittent of DER output and bidirectional power flow can be overcome with smart grid systems. In this study, an automation algorithm has been conducted to aid the engineers in solving the protection problems caused by DER implementation. The Protection Security Assessment (PSA) method is used to evaluate the state of the protection system. Determine the relay settings using an adaptive rule-based method on expert systems. The application with a Graphical User Interface (GUI) has been developed to make user easier to get the specific relay settings and locations which are sensitive, fast, reliable, and selective.

The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control decisions are generated based on the data the Smart Grid generates during its operation. This data can be tampered with or attacked in communication lines to mislead the control room in decision-making. The false data injection attack (FDIA) is one of the most severe cyberattacks on today’s cyber-physical power system, as it has the potential to cause significant physical and financial damage. However, detecting cyberattacks are incredibly challenging since they have no known patterns. In this paper, we launch a random FDIA on IEEE-39 bus system. Later, we propose a Bagging MLP-based autoencoder to detect the FDIAs in the power system and compare the result with a single ML model. The Bagging MLP-based autoencoder outperforms the Isolation forest while detecting FDIAs.

To ensure reliable and predictable service in the electrical grid it is important to gauge the level of trust present within critical components and substations. Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between substations using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between substations.

Aiming at the big data security and privacy protection issues in the smart grid, the current key technologies for big data security and privacy protection in smart grids are sorted out, and a privacy-protecting smart grid association rule is proposed according to the privacy-protecting smart grid big data analysis and mining technology route The mining plan specifically analyzes the risk factors in the operation of the new power grid, and discusses the information security of power grid users from the perspective of the user, focusing on the protection of privacy and security, using safe multi-party calculation of the support and confidence of the association rules. Privacy-protecting smart grid big data mining enables power companies to improve service quality to 7.5% without divulging customer private information.