Attack Context Embedded Data Driven Trust Diagnostics in Smart Metering Infrastructure

2021 ◽  
Vol 24 (2) ◽  
pp. 1-36
Author(s):  
Shameek Bhattacharjee ◽  
Venkata Praveen Kumar Madhavarapu ◽  
Simone Silvestri ◽  
Sajal K. Das
Keyword(s):  
Smart Grids ◽  
Location Parameter ◽  
Median Absolute Deviation ◽  
Coarse Grained ◽  
Smart Meters ◽  
Absolute Deviation ◽  
Detection Rates ◽  
Scoring Model ◽  
Electricity Theft ◽  
Attack Probability

Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains.

Malicious Node Detection in Smart Grid Networks

2021 ◽  
Author(s):  
Faisal Y Al Yahmadi ◽  
Muhammad R Ahmed
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Electricity Consumption ◽  
Cyber Attacks ◽  
Support Vector ◽  
Smart Meters ◽  
Malicious Nodes ◽  
High Detection Rate ◽  
Detection Model ◽  
Electricity Theft

Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.

scholarly journals A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5599
Author(s):  
Zeeshan Aslam ◽  
Nadeem Javaid ◽  
Ashfaq Ahmad ◽  
Abrar Ahmed ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ensemble Learning ◽  
Smart Grids ◽  
Power Efficiency ◽  
Short Term Memory ◽  
Support Vector ◽  
Smart Meters ◽  
Adaptive Boosting ◽  
Electricity Theft

Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

scholarly journals eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 2212-2222
Author(s):  
Alvincent E. Danganan ◽  
Edjie Malonzo De Los Reyes
Keyword(s):  
Data Analysis ◽  
Clustering Algorithm ◽  
Median Absolute Deviation ◽  
Accuracy Rate ◽  
Absolute Deviation ◽  
Improvement Rate ◽  
Overlapping Clustering ◽  
Overlapping Clusters ◽  
Runtime Performance

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

Vulnerabilities and Threats in Smart Grid Communication Networks

2021 ◽  
pp. 1508-1535
Author(s):  
Yona Lopes ◽  
Natalia Castro Fernandes ◽  
Tiago Bornia de Castro ◽  
Vitor dos Santos Farias ◽  
Julia Drummond Noce ◽  
...  
Keyword(s):  
Smart Grid ◽  
Communication Networks ◽  
Smart Grids ◽  
Electrical Network ◽  
Physical Security ◽  
Smart Meters ◽  
Interconnected System ◽  
Other Information ◽  
Data Volume ◽  
Active Attacks

Advances in smart grids and in communication networks allow the development of an interconnected system where information arising from different sources helps building a more reliable electrical network. Nevertheless, this interconnected system also brings new security threats. In the past, communication networks for electrical systems were restrained to closed and secure areas, which guaranteed network physical security. Due to the integration with smart meters, clouds, and other information sources, physical security to network access is no longer available, which may compromise the electrical system. Besides smart grids bring a huge growth in data volume, which must be managed. In order to achieve a successful smart grid deployment, robust network communication to provide automation among devices is necessary. Therefore, outages caused by passive or active attacks become a real threat. This chapter describes the main architecture flaws that make the system vulnerable to attacks for creating energy disruptions, stealing energy, and breaking privacy.

Implementation of Load Control for Smart Metering in Smart Grids

2022 ◽  
pp. 127-164
Author(s):  
Abdelmadjid Recioui ◽  
Fatma Zohra Dekhandji
Keyword(s):  
Smart Grids ◽  
Control Process ◽  
Electrical Energy ◽  
Load Control ◽  
Smart Meters ◽  
Cumulative Energy ◽  
Photovoltaic Panel ◽  
Labview Software ◽  
Data Acquisition Card ◽  
Cost Calculations

The conventional energy meters are not suitable for long operating purposes as they spend much human and material resources. Smart meters, on the other hand, are devices that perform advanced functions including electrical energy consumption recording of residential/industrial users, billing, real-time monitoring, and load balancing. In this chapter, a smart home prototype is designed and implemented. Appliances are powered by the grid during daytime, and a photovoltaic panel stored power during the night or in case of an electricity outage. Second, consumed power from both sources is sensed and further processed for cumulative energy, cost calculations and bill establishment for different proposed scenarios using LABVIEW software. Data are communicated using a USB data acquisition card (DAQ-USB 6008). Finally, a simulation framework using LABVIEW software models four houses each equipped with various appliances. The simulator predicts different power consumption profiles to seek of peak-demand reduction through a load control process.

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