AMI Data Encryption Method Based on Machine Learning and Aggregate Signature

The integrity and authenticity of the energy usage data in Advanced Metering Infrastructure (AMI) is crucial to ensure the correct energy load to facilitate generation, distribution and customer billing. Any malicious tampering to the data must be detected immediately. This paper introduces secure end-to-end data aggregation for AMI, a security protocol that allows the concentrators to securely aggregate the data collected from the smart meters, while enabling the utility back-end that receives the aggregated data to verify the integrity and data originality. Compromise of concentrators can be detected. The aggregated data is protected using Chameleon Signatures and then forwarded to the utility back-end for verification, accounting, and analysis. Using the Trapdoor Chameleon Hash Function, the smart meters can periodically send an evidence to the utility back-end, by computing an alternative message and a random value (m′, r) such that m′ consists of all previous energy usage measurements of the smart meter in a specified period of time. By verifying that the Chameleon Hash Value of (m′, r) and that the energy usage matches those aggregated by the concentrators, the utility back-end is convinced of the integrity and authenticity of the data from the smart meters. Any data anomaly between smart meters and concentrators can be detected, thus indicating potential compromise of concentrators.

The protection of smart meters (SMs) from cyberattacks is of utmost importance because SMs in advanced metering infrastructure (AMI) are physically unprotected and produce a large amount of sensitive data. Due to scalability, the SMs are small-sized and low-cost devices having low computational capabilities. The algorithms that are designed to complete the security requirements of SMs should be lightweight. To address this issue, this paper proposes a lightweight security solution to address the man-in-the-middle attack, data tempering, and blockchain-based data provenance. Received signal strength indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient ( $\rho $ ) of 0.9102 is achieved when no adversarial node is present in between the connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of $\rho $ are computed when the adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain-based data provenance, all the hash values of the packet header are 100% matched with the hash functions present at the data concentrator unit (DCU), which shows no adversary’s involvement in AMI. For cases when the adversary is in the AMI, hash functions show no match with the hash values present at the DCU.

Advanced Metering Infrastructure (AMI) is the aggregation of smart meters, communications networks, and data management systems that are tailored to meet the efficient integration of renewable energy resources. The more complex features and soundless functionalities the AMI is enhanced with, the more cyber security concerns are raised and must be taken into consideration. It is imperative to assure consumer’s privacy and security to guarantee the proliferation of rolling out smart metering infrastructure. This research paper analyzes AMI from security perspectives; it discusses the possible vulnerabilities associated with different attack surfaces in the smart meter, their security and threat implications, and finally it recommends proper security controls and countermeasures. The research findings draw the foundation upon which robust security by design approach is geared for the deployment of the AMI in the future.

With the substantial increase in implementation of green technology across all sectors worldwide to overcome energy loss, attributes such as reliability, efficiency, productivity, and security become very crucial for a system. Especially with the integration of IoT and smart features into a system alongside the evolution of Internet, cybersecurity and privacy of an individual component should be taken into important consideration. A smart grid comprises wireless network components exposed to cyber-attacks, smart meters being one the most vulnerable part of an advanced metering infrastructure. An advanced metering infrastructure enables two-way communication for automatic meter readings (AMR) and transmits data through wireless network. To counter cyber-attacks afflicting the network of AMI and functioning of a smart meter, additional intrusion detection methods are implemented into the grid network. The review includes cybersecurity concerns associated with smart meters along with an in-depth insight to some major cyber-attacks and their aftermath.KeywordsCybersecurityCyber-attacks advanced metering infrastructure (AMI)Smart metersDenial-of-service (DoS)False data injection (FDI)Man-in-the-middle (MitM)

Advanced metering infrastructure (AMI) is an enabling technology improving the quality of metering and transferring the energy consumption data in the electric energy network. Data with such quality acquired from a bidirectional infrastructure is a highly instrumental in increasing energy awareness in smart cities. The bidirectional communication infrastructure provided by AMI increases the customers' awareness about their consumption behavior, too. One of the issues affected by considering AMI in distribution network is demand side management (DSM). The accurate customer characterization using AMI data is a practical point in increasing energy awareness in the smart cities. The role of AMI in DSM programs is embossed when customers are regarded as influencing participants in these programs. This paper proposes a procedure investigating the role of AMI in improving demand response (DR) by costumer characterization in distribution network. Authors believe that DR is suitably implemented in distribution network if the analysis of customers' consumption behavior is conducted accurately. Besides, customers' capacities for participating in DR are different which is unfold by analyzing their consumption behaviors. Customers' consumption that are recorded by smart meters in different time slots compose time series on which pattern recognition procedures are applied. Different time series are clustered on the basis of their similarities and DR is implemented on each cluster separately. In This study, DR is implemented in two years in one of which capacities of each cluster for participation in DR is estimated and in another one these capacities are compared with the observed capacities. The results of this study confirm the accuracy of the estimated capacities and the proposed method. This knowledge helps the networks' operators to get a more accurate overview on the total capacity of the distribution network for implementing DR. The proposed method is implemented on a real dataset provided by Irish social science data archive (ISSDA).

Smart meters are key components of the smart grid infrastructure, connecting households to utility providers. With Internet connectivity to such devices, the smart grid Advanced Metering Infrastructure (AMI) is exposed to attacks by the adversary class. Any compromise of a smart meter may jeopardize the security of not just the household in question, but also the entire neighborhood network and possibly the utility provider's core network. One attack category that may pose a serious threat to the AMI is a meter compromise attack, wherein a smart meter is controlled by the adversary, with the intent of committing energy fraud. In this paper, we present a detection model based on a cumulative analysis of smart meter readings of clustered smart meters. The clustering of smart meters is done based on the physical deployment points of individual smart meters within a neighbourhood area network. Results obtained through simulation of the proposed scheme are promising.

Internet of Things connects people and things at any time and in any place. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most significant components in smart grid which collects data from smart meters and sends the collected data to the utility center to be analyzed and stored. To implement two-way communication between smart grid components, machine-to-machine communications has a great potential. In this paper, we propose a machine-to-machine architecture for advanced metering infrastructure in Internet of Things-based smart grid and focus on finding the optimum number of collectors based on two metrics: collectors' cost and packet delay. We model advanced metering infrastructure as a queuing system and derive an expression for packet delay. We also propose a newperformance metric, the product of cost and delay (CD), so that by minimizing it, we can find the optimum number of collectors. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost and delay.

Research on data security of multicast transmission based on certificateless multi-recipient signcryption in AMI

Effective utilization and management of energy has become an important issue throughout the world. This paper presents a methodology to effectively monitor and manage energy consumption by using advanced metering infrastructure (AMI). The electrical data such as voltage, current, power, and power factor that are derived from the energy utilization made by household appliances are considered to be the parameters for developing a smart meter, which is found to be the fundamental framework of AMI. These metered data are used to develop an AMI framework in a more intelligent and cost-effective manner. WiFi and Global System for Mobile communication network are integrated within the system so as to facilitate better communication between the consumer and electricity board. The developed system can act as a candidate solution to detect the energy theft from demand side, and it is achieved with the help of support vector machine technique. This paper also proposes a prepaid utilization of energy on demand basis, which could greatly reduce the power consumption levels.

The demand for enhanced energy management systems is being driven by the desire for sustainable development and the rapid evolution of energy consumption patterns. This study examines the potential for advanced metering infrastructure (AMI) and smart meters to enhance the efficacy of energy transmission and consumption. Smart meters enable customers to monitor their energy consumption in real time, thereby increasing their awareness of their actions and motivating them to adopt more environmentally favorable behaviors. These technologies facilitate two-way communication between utilities and end consumers when integrated with AMI. These features include demand response, automatic invoicing, dynamic pricing, and power outage monitoring. The integration of data analytics and the Internet of Things by AMI enhances the environmental sustainability, operational efficiency, and reliability of the grid. This paper investigates the design, advantages, disadvantages, and potential future improvements of smart energy systems that are powered by AMI and smart meters. We are of the opinion that these technologies will be beneficial in the creation of an intelligent and resilient energy system

Public-key cryptography (PKC) is widely used in smart grid (SG) communications to reduce the overhead of key management. However, PKC comes with its own problems in terms of certificate management. Specifically, certificate revocation lists (CRLs) need to be maintained and distributed to the smart meters (SMs) in order to ensure security of the communications. The size of CRLs may grow over time and eventually may introduce additional delay, bandwidth, and storage overhead when various applications are run on SG. In this paper, we propose novel algorithms for creating customized CRLs with reduced size for IEEE 802.11s-based advanced metering infrastructure (AMI) networks. Rather than maintaining a huge-size single CRL that introduces unnecessary search time and storage, the idea is to cluster/group SMs within the AMI network and create CRLs based on these groups. The grouping is mainly done in such a way that they bring together the SMs that will be very likely to communicate so that the CRLs will be kept local to that group. To this end, we propose two novel grouping algorithms. The first algorithm is a bottom-up approach, which is based on the existing routes from the SMs to the gateway. Since the SMs will be sending their data to the gateway through the nodes on the route, this forms a natural grouping. The second approach is a top-down recursive approach, which considers the minimum spanning tree of the network and then divides it into smaller subtrees. Via grouping, the length of the CRL for each SM and the corresponding distribution overhead can be reduced significantly. Simulation results have shown that our approach can maintain a balance between the size of the CRL and the number of signatures generated by CAs while guaranteeing security of the communications.

This Paper examines, in detail, all aspects related to rollout of Advanced Metering Infrastructure (AMI) in India. It covers key issues such as smart meter standards and specification, testing of smart meters, retrofitting of existing meters, communication technology options, procurement strategy and business models for AMI rollout. Under the UDAY program, Government of India plans to deploy smart meters for all customers with monthly consumption above 200 kWh by December 2019. Distribution Companies (DISCOMs) have the responsibility of choosing the most optimum deployment strategy. While the traditional AMI approach proposes deployment of smart meters for all customers on a feeder, another option is to deploy only for customers having monthly electricity consumption greater than 200 units. Part A of this Paper presents a detailed cost-benefit analysis which strongly advocates deployment of smart meters for all customers on a feeder primarily because of the potential to reduce Aggregate Technical and Commercial (AT&C) losses and the substantially lower cost for the last mile communication network when the full feeder is covered. Part B of this Paper covers a set of recommendations for AMI roll out in India.

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

Advanced Metering Infrastructure (AMI) enables two-way communication between smart devices and utility control centers. This involves remote monitoring and control of energy consumption as well as other parameters in the electrical power network in real time. However, increasing technologies in AMI due to huge deployment of smart meters, integration of devices and application of sensors, demand a strong architectural model with the best network topology to guarantee efficient usage of network resources with minimal latency. In this work, a resource efficient multi-hop network architecture is proposed using hybrid media access protocols. The architecture combines queuing and random-access protocol to achieve optimal network performance. Numerical results show that the probability of delay incurred by an arbitrary smart meter depends on the mean and distribution of the queue switch over a period. It is also observed that for a single queued system, the throughput performance is equal to the existing hybrid method. As the number of smart meters increases to 500, the throughput of the proposed method improves by 10% compared to the existing method. Likewise, as the number of smart meters increases to 500, the delay reduced by 15% compared to the existing method. Keywords: Advanced Metering Infrastructure; hybrid media access protocols; Smart Meter; Smart Grid; Power Network.

A computer worm is malicious software that can spread rapidly between interconnected devices and perform malicious actions. In an Advanced Metering Infrastructure (AMI), smart meters may communicate in an ad-hoc fashion to perform many functionalities like remote metering and demand response. However, the AMI is susceptible to cyber-physical threats caused by malware like worms. The speed at which worms propagate in the AMI affects the physical consequences of cyber attacks. In this paper, worm propagation in the AMI is simulated and a probabilistic model is derived. This model can be used to estimate the time required to infect N meters in the AMI and accordingly predict possible physical consequences.