Biblio

We have proposed a new Smart Meter Application (SMA) Framework. This application registers consumers at utility provider (Electricity), takes the meter reading for electricity and makes billing. The proposed application might offer higher level of flexibility and security, time saving and trustworthiness between consumers and authority offices. It’s expected that the application will be developed by Flutter to support Android and iOS Mobile Operating Systems.

Integration of technology with power grid emerged Smart grid. The advancement of power grid into smart grid faces some security issues like message mod-ification attacks, message injection attacks etc. If these issues are correctly not addressed, then the performance of the smart grid is degraded. Smart grid has bidirectional communication among the smart grid entities. The flow of user energy consumption information between all smart grid entities may lead the user privacy violation. Smart grids have various components but service providers and smart meters are the main components. Smart meters have sensing and communication functionality, while service providers have control and communication functionality. There are many privacy preservation schemes proposed that ensure the cus-tomer's privacy in the smart grid. To preserve the customer's data privacy and communication, authentication and key agreement schemes are required between the smart meter and the service provider. This paper proposes an efficient key agreement protocol to handle several security challenges in smart grid. The proposed protocol is tested against the various security attributes necessary for a key establishment protocol and found safe. Further the performance of the proposed work is compared with several others existing work for smart grid application and it has been observed that the proposed protocol performs significantly better than the existing protocols available in the literature.

Managing electricity effectively also means knowing as accurately as possible when, where and how electricity is used. Detailed metering and timely allocation of consumption can help identify specific areas where energy consumption is excessive and therefore requires action and optimization. All those interested in the measurement process (distributors, sellers, wholesalers, managers, ultimately customers and new prosumer figures - producers / consumers -) have an interest in monitoring and managing energy flows more efficiently, in real time.Smart meter plays a key role in sending data containing consumer measurements to both the producer and the consumer, thanks to chain 2. It allows you to connect consumption and production, during use and the customer’s identity, allowing billing as Time-of-Use or Real-Time Pricing, and through the new two-way channel, this information is also made available to the consumer / prosumer himself, enabling new services such as awareness of energy consumption at the very moment of energy use.This is made possible by latest generation devices that "talk" with the end user, which use chain 2 and the power line for communication.However, the implementation of smart meters and related digital technologies associated with the smart grid raises various concerns, including, privacy. This paper provides a comparative perspective on privacy policies for residential energy customers, moreover, it will be possible to improve security through the blockchain for the introduction of smart contracts.

Smart grid (SG) network is one of the recently improved networks of tangled entities, objects, and smart metering infrastructure (SMI). It plays a vital part in sensing, acquiring, observing, aggregating, controlling, and dealing with various kinds of fields in SG. The SMI or advanced metering infrastructure (AMI) is proposed to make available a real-time transmissions connection among users and services are Time of use (TOU), Real time pricing (RTP), Critical Peak Pricing (CPP). In adding to, additional benefit of SMs is which are capable to report back to the service control center in near real time nontechnical losses (for instance, tampering with meters, bypassing meters, and illicit tapping into distribution systems). SMI supports two-way transmission meters reading electrical utilization at superior frequency. This data is treated in real time and signals send to manage demand. This paper expresses a transitory impression of cyberattack instances in customary energy networks and SMI. This paper presents cyber security attacks and countermeasures in Smart Grid Metering Network (SGMN). Based on the existing survey threat models, a number of proposed ways have been planned to deal with all threats in the formulation of the secrecy and privacy necessities of SG measurement network.

With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9.25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.

Looking at the Smart Grid as a Cyber - Physical system of great complexity, the paper synthesizes the main IT security issues that may arise. Security issues are seen from a hybrid point of view, combining theory of information with system theory. Smart Grid has changed dramatically over the past years. With modern technologies, such as Big Data or Internet of Things (IoT), the Smart Grid is evolving into a more interconnected and dynamic power network model.

The generation, transmission, distribution, and storage of electric power is becoming increasingly decentralized. Advances in Distributed Energy Resources (DERs) are rapidly changing the nature of the power grid. Moreover, the accommodation of these new technologies by the legacy grid requires that an increasing number of devices be Internet connected so as to allow for sensor and actuator information to be collected, transmitted, and processed. With the wide adoption of the Internet of Things (IoT), the cybersecurity vulnerabilities of smart grid devices that can potentially affect the stability, reliability, and resilience of the power grid need to be carefully examined and addressed. This is especially true in situations in which smart grid devices are deployed with default configurations or without reasonable protections against malicious activities. While much work has been done to characterize the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices, this paper demonstrates that similar vulnerabilities associated with the newer class of IoT smart grid devices are becoming a concern. Specifically, this paper first performs an evaluation of such devices using the Shodan platform and text processing techniques to analyze a potential vulnerability involving the lack of password protection. This work further explores several Shodan search terms that can be used to identify additional smart grid components that can be evaluated in terms of cybersecurity vulnerabilities. Finally, this paper presents recommendations for the more secure deployment of such smart grid devices.

Smart grid has evolved as the next generation power grid paradigm which enables the transfer of real time information between the utility company and the consumer via smart meter and advanced metering infrastructure (AMI). These information facilitate many services for both, such as automatic meter reading, demand side management, and time-of-use (TOU) pricing. However, there have been growing security and privacy concerns over smart grid systems, which are built with both smart and legacy information and operational technologies. Intrusion detection is a critical security service for smart grid systems, alerting the system operator for the presence of ongoing attacks. Hence, there has been lots of research conducted on intrusion detection in the past, especially anomaly-based intrusion detection. Problems emerge when common approaches of pattern recognition are used for imbalanced data which represent much more data instances belonging to normal behaviors than to attack ones, and these approaches cause low detection rates for minority classes. In this paper, we study various machine learning models to overcome this drawback by using CIC-IDS2018 dataset [1].

The roll-out of smart meters (SMs) in the electric grid has enabled data-driven grid management and planning techniques. SM data can be used together with short-term load forecasts (STLFs) to overcome polling frequency constraints for better grid management. However, the use of SMs that report consumption data at high spatial and temporal resolutions entails consumer privacy risks, motivating work in protecting consumer privacy. The impact of privacy protection schemes on STLF accuracy is not well studied, especially for smaller aggregations of consumers, whose load profiles are subject to more volatility and are, thus, harder to predict. In this paper, we analyse the impact of two user demand shaping privacy protection schemes, model-distribution predictive control (MDPC) and load-levelling, on STLF accuracy. Support vector regression is used to predict the load profiles at different consumer aggregation levels. Results indicate that, while the MDPC algorithm marginally affects forecast accuracy for smaller consumer aggregations, this diminishes at higher aggregation levels. More importantly, the load-levelling scheme significantly improves STLF accuracy as it smoothens out the grid visible consumer load profile.

Cyber security has been a top concern for electric power companies deploying smart meters and smart grid technology. Despite the well-known advantages of smart grid technology and the smart meters, it is not yet very clear how and to what extent, the Cyber attacks can hamper the operation of the smart meters, and remote data collections regarding the power usage from the customer sites. To understand these questions, we conducted experiments in a controlled lab environment of our cyber security lab to test a commercial grade smart meter. In this paper, we present results of our investigation for a commercial grade smart meter and measure the operation integrity of the smart meter under cyber-attack conditions.

Due to the rise of huge population in mankind and the large variety of upcoming utilization of power, the energy requirement has substantially increased. Smart Grid is a very important part of the Smart Cities initiative and is one of the crucial components in distribution and reconciliation of energy. Security of the smart grid infrastructure, which is an integral part of the smart grid framework, intended at transitioning the conventional power grid system into a robust, reliable, adaptable and intelligent energy utility, is an impending problem that needs to be arrested quickly. With the increasingly intensifying integration of smart devices in the smart grid infrastructure with other interconnected applications and the communication backbone is compelling both the energy users and the energy utilities to thoroughly look into the privacy and security issues of the smart grid. In this paper, we present challenges of the existing security mechanisms deployed in the smart grid framework and we tried to bring forward the unresolved problems that would highlight the security aspects of Smart Grid as a challenging area of research and development in the future.

Smart Grid (SG) technology has been developing for years, which facilitates users with portable access to power through being applied in numerous application scenarios, one of which is the electric vehicle charging. In order to ensure the security of the charging process, users need authenticating with the smart meter for the subsequent communication. Although there are many researches in this field, few of which have endeavored to protect the anonymity and the untraceability of users during the authentication. Further, some studies consider the problem of user anonymity, but they are non-light-weight protocols, even some can not assure any fairness in key agreement. In this paper, we first points out that existing authentication schemes for Smart Grid are neither lack of critical security nor short of important property such as untraceability, then we propose a new two-factor lightweight user authentication scheme based on password and biometric. The authentication process of the proposed scheme includes four message exchanges among the user mobile, smart meter and the cloud server, and then a security one-time session key is generated for the followed communication process. Moreover, the scheme has some new features, such as the protection of the user's anonymity and untraceability. Security analysis shows that our proposed scheme can resist various well-known attacks and the performance analysis shows that compared to other three schemes, our scheme is more lightweight, secure and efficient.

By applying power usage statistics from smart meters, users are able to save energy in their homes or control smart appliances via home automation systems. However, owing to security and privacy concerns, it is recommended that smart meters (SM) should not have direct communication with smart appliances. In this paper, we propose a design for a smart meter gateway (SMGW) associated with a two-phase authentication mechanism and key management scheme to link a smart grid with smart appliances. With placement of the SMGW, we can reduce the design complexity of SMs as well as enhance the strength of security.

As the key component of the smart grid, smart meters fill in the gap between electrical utilities and household users. Todays smart meters are capable of collecting household power information in real-time, providing precise power dispatching control services for electrical utilities and informing real-time power price for users, which significantly improve the user experiences. However, the use of data also brings a concern about privacy leakage and the trade-off between data usability and user privacy becomes an vital problem. Existing works propose privacy-utility trade-off frameworks against statistical inference attack. However, these algorithms are basing on distorted data, and will produce cumulative errors when tracing household power usage and lead to false power state estimation, mislead dispatching control, and become an obstacle for practical application. Furthermore, previous works consider power usage as discrete variables in their optimization problems while realistic smart meter data is continuous variable. In this paper, we propose a mechanism to estimate the trade-off between utility and privacy on a continuous time-series distorted dataset, where we extend previous optimization problems to continuous variables version. Experiments results on smart meter dataset reveal that the proposed mechanism is able to prevent inference to sensitive appliances, preserve insensitive appliances, as well as permit electrical utilities to trace household power usage periodically efficiently.

The Internet of Things (IoT) paradigm, in conjunction with the one of smart cities, is pursuing toward the concept of smart buildings, i.e., “intelligent” buildings able to receive data from a network of sensors and thus to adapt the environment. IoT sensors can monitor a wide range of environmental features such as the energy consumption inside a building at fine-grained level (e.g., for a specific wall-socket). Some smart buildings already deploy energy monitoring in order to optimize the energy use for good purposes (e.g., to save money, to reduce pollution). Unfortunately, such measurements raise a significant amount of privacy concerns. In this paper, we investigate the feasibility of recognizing the pair laptop-user (i.e., a user using her own laptop) from the energy traces produced by her laptop. We design MTPlug, a framework that achieves this goal relying on supervised machine learning techniques as pattern recognition in multivariate time series. We present a comprehensive implementation of this system and run a thorough set of experiments. In particular, we collected data by monitoring the energy consumption of two groups of laptop users, some office employees and some intruders, for a total of 27 people. We show that our system is able to build an energy profile for a laptop user with accuracy above 80%, in less than 3.5 hours of laptop usage. To the best of our knowledge, this is the first research that assesses the feasibility of laptop users profiling relying uniquely on fine-grained energy traces collected using wall-socket smart meters.

Non-Technical Loss (NTL) fraud is a very common fraud in power systems. In traditional power grid, energy theft, via meter tampering, is the main form of NTL fraud. With the rise of Smart Grid, adversaries can take advantage of two-way communication to commit NTL frauds by meter manipulation or network intrusion. Previous schemes were proposed to detect NTL frauds but are not efficient. In this paper, we propose a Fast NTL Fraud Detection and verification scheme (FNFD). FNFD is based on Recursive Least Square (RLS) to model adversary behavior. Experimental results show that FNFD outperforms existing schemes in terms of efficiency and overhead.

In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only.

Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information. In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20%-10%.

More and more intelligent functions are proposed, designed and implemented in meters to make the power supply be smart. However, these complex functions also bring risks to the smart meters, and they become susceptible to vulnerabilities and attacks. We present the rat-group attack in this paper, which exploits the vulnerabilities of smart meters in the cyber world, but spreads in the physical world due to the direct economic benefits. To the best of our knowledge, no systematic work has been conducted on this attack. Game theory is then applied to analyze this attack, and two game models are proposed and compared under different assumptions. The analysis results suggest that the power company shall follow an open defense policy: disclosing the defense parameters to all users (i.e., the potential attackers), results in less loss in the attack.