Advanced Metering Infrastructure Network Research Articles

A Detection and Response Architecture for Stealthy Attacks on Cyber-Physical Systems

There has been an increased reliance on interconnected Cyber-Physical Systems (CPS) applications. This reliance has caused tremendous growth in high assurance challenges. Due to the functional interdependence between the internal systems of CPS applications, the utilities' ability to reliably provide services could be disrupted if security threats are not addressed. To address this challenge, we propose a multi-level, multi-agent detection and response architecture built on the formalisms of Hidden Markov Models (HMM) and Markov Decision Processes (MDP). We have evaluated the performance of the proposed architecture on one of the critical smart grid applications, Advanced Metering Infrastructure (AMI). This paper utilizes a simulation tool called SecAMI for performance evaluation. A Stealthy attack scenario contains multiple distinct multi-stage attacks deployed concurrently in a network to compromise the system and stop several critical services in a CPS. The results show that the proposed architecture effectively detects and responds to stealthy attack scenarios against Cyber-Physical Systems. In particular, the simulation results show that the proposed system can preserve the availability of more than 93% of the AMI network under stealthy attacks. A future study may evaluate the effectiveness of various stealthy attack strategies and detection and response systems. The high availability of any AMI should be protected against new attack techniques. The proposed system will also determine a distributed IDS's efficient placement for intrusion detection sensors and response nodes within an AMI.

Securing AMI-IoT networks against multiple RPL attacks using ensemble learning IDS and light-chain based prediction detection and mitigation mechanisms

ABSTRACT Advanced Metering Infrastructure (AMI) is one of the Internet of Things (IoT) enabled smart applications of smart grids. The Routing Protocol for Low Power and Lossy network (RPL) has been accepted to facilitate effective routing services for the AMI. However, numerous RPL attacks appear in AMI due to resource scarcity and dubious wireless medium, which significantly impedes the successful deployment of AMI-RPL. To enable secure and reliable AMI-RPL, this work proposes a novel Intrusion Detection System (IDS) named AMI Attack-aware Intelligent Machine learning IDS (AIMS). The primary objectives of AIMS are to predict, detect, and mitigate different types of RPL security attacks in the AMI environment. To predict the RPL attacks using the Stacked Ensemble (SE) machine learning model, a novel AMI-RPL Attack Dataset (ARAD) is generated by the Cooja simulator with the suitable pre-processing and the Spider Monkey Optimization (SMO) based feature selection. The advanced prediction of attack nodes improves the performance and significantly diminishes the future damages of AMI. The attack detection is based on immutable blocks of a light-chain model, and the cryptocurrency-based mitigation model effectively isolates the attackers. AIMS mechanism amplifies RPL security with high reliability and maximizes the AMI network lifetime by delivering superior results.

Improving the Reliability of RPL Using Hybrid Deep Learning and Objective Function-Based DODAG Structure for AMI

Advanced Metering Infrastructure (AMI) is the prime smart grid application that connects smart meters and electric power stations. Routing Protocol for Low-Power and Lossy Networks (RPL) is the most familiar lightweight routing protocol for AMI networks. The reliability of RPL routing is a potential problem for the efficient deployment of AMI networks. This paper ensures attack and network reliability for AMI-RPL and proposes Hybrid Deep Learning based Intrusion Detection System (HDL-IDS) for attack reliability and a Multi-Objective Function-based Reliable RPL (MOR-RPL) for network reliability. The HDL-IDS assures AMI-RPL reliability against attacks by analyzing and eliminating the attack traffic successfully. The MOR-RPL method improves the RPL construction reliability by procuring the multi-objective function-based reliability metrics in the DODAG building. The simulation results show that the attack and network reliability are significantly enhanced with balanced energy consumption.

Electricity-Theft Detection for Change-and-Transmit Advanced Metering Infrastructure

The periodic transmission of the customers’ power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers’ transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector’s ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.

Secure advance metering infrastructure protocol for smart grid power system enabled by the Internet of Things

The incorporation of Advanced Metering Infrastructure (AMI) into Smart Grid applications has piqued the interest of Internet of Things (IoT) experts, who have aided smart city initiatives. One of the fundamental building components of AMI for information sharing is wireless communication networks. Wireless communication networks are prone to a variety of security threats, which causes issues when developing the AMI system in smart city projects. Confidentiality, integrity, availability, and privacy are the primary criteria of cyber-physical systems such as AMI. To meet the needs of cyber-physical systems, we worked on a wireless communication protocol that solves data transmission dependability and data security while consuming little power and overhead. We proposed a novel Secure and Reliable AMI (SRAMI) protocol in this paper using a lightweight cryptographic technique and trust-based mechanism for reliability for Wireless Sensor Network (WSN)-assisted AMI. The trust-based approach presents a trustworthy data transfer technique between two communicating AMI entities. In this regard, the next-hop is chosen based on trust-evaluation characteristics such as energy expenditure, geographical distance, and bandwidth availability. Furthermore, a lightweight hybrid approach based on Elliptic Curve Cryptography (ECC) is introduced to meet the data integrity and privacy requirements in the AMI network. SRAMI's performance was improved compared to state-of-the-art protocols in terms of throughput, Packet Delivery Ratio (PDR), average energy consumption, latency and communication overhead.

Smart Meter Pinging and Reading Through AMI Two-Way Communication Networks to Monitor Grid Edge Devices and DERs

Today’s power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with two-way communications presents great potential. At present, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This paper discusses the AMI two-way communication and its recent industrial practice in the U.S., especially for applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then develops the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems.

A rule-based model for electricity theft prevention in advanced metering infrastructure

The deployment of smart electricity meter (SEM) via the advanced metering infrastructure (AMI) has come under cyber-attacks as adversaries continue to exploit the communication links for possible evasion of electricity bill payments. Various detection models relying on energy consumption data offer a disadvantage of delayed detection and consequent huge financial losses before frauds are detected. Moreover, existing techniques mostly concentrate on detection of electricity thefts and rely on energy consumption data alone as the basis of theft perpetration whereas other potential parameters which could be exploited for electricity theft prevention exist in AMI. In this study, AMI parameters, which are indicative of electricity thefts are preselected and modelled for electricity theft prevention. First, a given AMI network is sectioned into zones with the selected parameters modelled to define security risks by formulated set of rules based on real-time scenarios. Fuzzy inference system is then employed to model the security risks to ascertain the compromised state of the monitored parameters at the defined scenarios. The result of the developed model at 50% weight of each of the modelled parameters with interdependencies show clear indications of the modelled parameters and their interactions in the determination of risks. The decisions on monitored parameters evaluated at every timestep reveal varied dense velocity behaviours for every scenario. The result is suitable for monitoring the AMI in reporting and/or disconnecting any compromised SEM within a considerable timestep before huge losses are incurred. Implementation of this scheme will contribute a significant success in the attempt to prevent electricity theft perpetration via the AMI.

Novel scheme for reducing communication data traffic in advanced metering infrastructure networks

Novel scheme for reducing communication data traffic in advanced metering infrastructure networks

An overview of DLMS/COSEM and g3-plc for smart metering applications

Abstract A crucial part of the smart grid communication network is the Advanced Metering Infrastructure (AMI), connecting a utility company to end-users to support telemetry and remote-control applications. Although different technologies and standards for smart metering systems exist, Power-Line Communication is the preferred technology for AMI networks in dense urban areas, and the G3-PLC, which uses DLMS/COSEM to support data exchange, is the most used standard in PLC-based AMI networks. This paper provides a holistic view of G3-PLC in smart metering systems, including its interaction with the DLMS/COSEM. It also covers the challenges and research opportunities in G3-PLC smart metering systems.

A Concealed Based Approach for Secure Transmission in Advanced Metering Infrastructure

The smart grid has an important subsystem known as the Advanced Metering Infrastructure (AMI), responsible for measuring customer consumption of electricity. The AMI subsystem has smart meters as one of the components and they play a vital role in enabling communication between the utility provider and consumers. Smart meters can report real-time electricity consumption readings of the consumer to the utility. Securing this communication link from attacks remain very important for the secure transmission of readings. Different methods or approaches have been developed in the past and most of the works have high computational overheads. This paper proposes a lightweight Concealed Based Security Scheme (CBSS) for secure transmission within the AMI, providing authentication, reducing the computational overheads and energy consumption during transmission. The CBSS method is compared with the AMI Data Communication Scheme (ADCS) which does not have authentication process. The network is built in the Network Simulator 2 environment, showing the communication between the nodes. Further security is provided using a simple encryption/decryption method of 2 random numbers. The contribution of this paper is the proposed lightweight method for the AMI that authenticates the transfer of data between smart meters and other components of the AMI system. The paper also contains a simulation evaluation for the chosen design parameters of the resemblance of AMI network. The simulations show an improvement of 5% in delivery ratio, 3% throughput and 4% on energy consumed when adding security to the network.

Privacy Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

The use of supercapacitor in smart metering gateway

Advanced Metering Infrastructure (AMI) consists smart meter, gateway, meter data management software. Smart meter and gateway work together to measure energy on the field. Then, gateway send the measured smart meter data that can be periodic or instant meter reading results, power factor, voltage-current-power information. In addition, gateway sends the information to the meter data management system whether there is energy or not by using a communication method in the field. If there is an integration between AMI and other distribution network systems like Outage Management System (OMS), Customer Relations Management, AMI provides measured data to these systems. Supercapacitor (SC) is a new type small energy storage device and it can easily use power system to provide the energy certain time in case of power failure. The gateway that is part of AMI connects the distribution grid physically on the field and provides own energy so when the power failure occurs in distribution grid at same time gateway energy will be cut. This means last measured data and last field situation can’t be send to the AMI software so all system integration and remote meter operation will halt. This paper handles SC usage as a part of AMI system gateway on the field to provide last measured data to the AMI software. SC benefits are shown using AMI system with measured data in distribution grid. SC model will explain considering gateway design. Gateway with SC and gateway without SC situations will compare in terms of AMI and other system integrations.

Smart Water Grid Research Group Project: An Introduction to the Smart Water Grid Living-Lab Demonstrative Operation in YeongJong Island, Korea

In South Korea, in line with the increasing need for a reliable water supply following the continuous increase in water demand, the Smart Water Grid Research Group (SWGRG) was officially launched in 2012. With the vision of providing water welfare at a national level, SWGRG incorporated Information and Communications Technology in its water resource management, and built a living lab for the demonstrative operation of the Smart Water Grid (SWG). The living lab was built in Block 112 of YeongJong Island, Incheon, South Korea (area of 17.4 km2, population of 8000), where Incheon International Airport, a hub for Northeast Asia, is located. In this location, water is supplied through a single submarine pipeline, making the location optimal for responses to water crises and the construction of a water supply system during emergencies. From 2017 to 2019, ultrasonic wave type smart water meters and IEEE 802.15.4 Advanced Metering Infrastructure (AMI) networks were installed at 527 sites of 958 consumer areas in the living lab. Therefore, this study introduces the development of SWG core element technologies (Intelligent water source management and distribution system, Smart water distribution network planning/control/operation strategy establishment, AMI network and device development, Integrated management of bi-directional smart water information), and operation solutions (Smart water statistics information, Real-time demand-supply analysis, Decision support system, Real-time hydraulic pipeline network analysis, Smart DB management, and Water information mobile application) through a field operation and testing in the living lab.

Real-time energy data compression strategy for reducing data traffic based on smart grid AMI networks

In the future, the Internet of things (IoT) may provide huge volumes of data. Smart grids are a class of IoT electricity distribution systems that can control bidirectional energy flows between consumers and service providers (Barman et al. “IOT Based Smart Energy Meter for Efficient Energy Utilization in Smart Grid”, 978-1-5386-4769-1 1831.00, 2018 IEEE). A typical smart grid features an advanced metering infrastructure (AMI), which automatically collects meter data from widely distributed sensors. A utility company that intends to use AMI must deploy smart meters, data concentrator units, and a meter data management system (MDMS). Concentrators collect ubiquitous messages from smart meters and transmit aggregated data to their MDMS. Although ubiquitous messages may enhance the efficiency of some electricity grids, any excessive volume of messages causes data congestion. This research considers the Reference Energy Disaggregation Data Set. Our proposed algorithm can compress meter data efficiently. Our contributions are as follows: First, thus far, numerous researchers have attempted to address smart grid problems with AMI systems, and here, we provide a relatively complete review of these attempts. Second, this paper presents a strategy to analyze this problem and propose a compression algorithm to compress meter data. This proposed algorithm combines several existing compression algorithms and operates from 2 to 10% more efficiently than previously published algorithms.

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach.

Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.

Analysis of AMI Communication Methods in Various Field Environments

In order to construct an efficient on-site communication network for an advanced metering infrastructure (AMI) in Korea, the high-speed power line communication (HS PLC), wireless smart utility network (Wi-SUN), and ZigBee modems are currently being used. In this paper, we first quantitatively analyze the communication performances of HS PLC, Wi-SUN, and ZigBee modems for AMI through both experimental testbeds and practical environment sites. For practical AMI sites, we selected 18 sites with 48 measurement points and classified the sites into five areas, and conducted measurements of signal and noise power spectra on the sites. We then derived linear regression models for received powers according to areas. Through the constructed models, we can efficiently choose an appropriate communication method and plan a methodology for building an AMI network depending on the area type. Furthermore, using the constructed regression models, we provided graphical simulation tools of received powers for both PLC and wireless communication methods based on a distribution information map.

Jamming-Resilient Backup Nodes Selection for RPL-based Routing in Smart Grid AMI Networks

Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time of data transmission. The IPv6 routing protocol for low-power and lossy networks (RPL), proposed by IETF ROLL working group, is considered to be the best routing solution for the AMI communication environment. However, the performance of RPL can be seriously degraded due to jamming attack. In this paper, we analyze the performance degradation problem of RPL protocol under jamming attack. We propose a backup node selection mechanism based on the standard RPL protocol. The proposed mechanism chooses a predefined number of backup nodes that maximize the probability of successful transmission. We evaluation the proposed mechanism through MATLAB simulations, results show the proposed mechanism improves the performance of RPL under jamming attack prominently.

ECS‐CP‐ABE: A lightweight elliptic curve signcryption scheme based on ciphertext‐policy attribute‐based encryption to secure downlink multicast communication in edge envisioned advanced metering infrastructure networks

AbstractAs an integral part of the smart grid, advanced metering infrastructure (AMI) implements two‐way communication to enable the smart grid achieving the desired performance improvements over the legacy grid. Integrating communication in addition to the inherited weakness of the power grid paves the way to countless number of security threats to target its communication networks, therefore ensuring secure data exchange is a key concern for AMI networks. In this regard, many schemes are proposed in the literature to secure this two‐way communication, however, very little effort has been focused on multicast transmissions carried out between the control center and a group of smart meters. In order to empower efficient, authentic, and confidential data exchange, this article proposes a new signcryption scheme for multicast downlink communication in edge envisioned AMI networks. The proposed scheme is constructed based on ciphertext‐policy attribute‐based encryption to attain secure fine‐grained access control for the multirecipient communication between the utility control center and a group of smart meters. The novelty of our scheme lies behind the fact that it is an elliptic curve cryptosystem that is pairing free, in opposite to most ABE schemes. Instead of the complex bilinear pairing operations, we exploited faster elliptic curve point multiplication operations to construct the proposed signcryption model. we demonstrate the strength of our protocol in defending against passive, collusion, reply, data modification, and signature forgery attacks. Simulation results prove the efficiency of our protocol in terms of messages size and computation complexity when compared with other schemes.

Using ID-Based Authentication and Key Agreement Mechanism for Securing Communication in Advanced Metering Infrastructure

Smart metering technology plays a key role in Advanced Metering Infrastructure (AMI) in Smart Grid (SG). Smart Meters (SM) measure Power Consumption Data (PCD) of household devices and send it to DSO (Distributed System Operator) for further processing. DSO utilizes PCD for different applications such as monthly billing, demand response and other applications related to power system operation and energy markets. Secure communication between SM and DSO is of paramount importance. Certificate-based authentication is a de facto mechanism in ensuring legitimacy of communicating parties. But it incurs more computational delay as it involves intensive processes such as certificate management, revocation, and verification. ID-based authentication eliminates the risks associated with certificate management. A key agreement protocol based on ID-based authentication mechanism is proposed and analyzed for computational performance. This paper analyzes and evaluates both authentication mechanisms: certificate and ID-based mechanism based on computational times for suitability in AMI network. The experimental results show that ID-based authentication and key agreement mechanism are suitable for securing communication in AMI network.

MDMS: Efficient and Privacy-Preserving Multidimension and Multisubset Data Collection for AMI Networks

Advanced metering infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers’ activities. Privacy-preserving data aggregation techniques have been used to preserve consumers’ privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multidimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multisubset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this article, we propose an efficient and privacy-preserving multidimensional and multisubset data collection scheme, named “MDMS. ” In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks’ gateways using the encrypted readings and following the dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers’ readings.