The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Mobile cloud computing is applied in multiple industries to obtain cloud-based services by leveraging mobile technologies. With the development of the wireless networks, defending threats from wireless communications have been playing a remarkable role in the Web security domain. Intrusion detection system IDS is an efficient approach for protecting wireless communications in the Fifth Generation 5G context. In this paper, we identify and summarize the main techniques being implemented in IDSs and mobile cloud computing with an analysis of the challenges for each technique. Addressing the security issue, we propose a higher level framework of implementing secure mobile cloud computing by adopting IDS techniques for applying mobile cloud-based solutions in 5G networks. On the basis of the reviews and synthesis, we conclude that the implementation of mobile cloud computing can be secured by the proposed framework because it will provide well-protected Web services and adaptable IDSs in the complicated heterogeneous 5G environment. Copyright © 2015John Wiley & Sons, Ltd.

Intrusion detection techniques for mobile cloud computing in heterogeneous 5G

In this paper, a framework for recognizing network traffic in order to detect anomalies is proposed. We propose to combine and correlate parameters from different layers in order to detect 0-day attacks and reduce false positives. Moreover, we propose to combine statistical and signal-based features. The major contribution of this paper is novel framework for network security based on the correlation approach as well as new signal-based algorithm for intrusion detection on the basis of the Matching Pursuit (MP) algorithm. As to our best knowledge, we are the first to use MP for intrusion and anomaly detection in computer networks. In the presented experiments, we proved that our solution gives better results than intrusion detection based on discrete wavelet transform. © 2012 Wiley Periodicals, Inc.

Statistical and signal-based network traffic recognition for anomaly detection

In this paper, we present network anomaly detection with the use of ARFIMA model. We propose the method of estimation parameters using the Hyndman-Khandakar algorithm to estimate the polymonials parameters and the Haslett and Raftery algorithm to estimate the differencing parameters. The choice of optimal values of the model parameters is performed on the basis of information criteria representing a compromise between the consistency model and the size of its error of estimate. In the presented method, we propose to use statistical relationships between predicted and original network traffic to determine if the examined trace is normal or attacked. The efficiency of our method is verified with the use of extended set of benchmark test real traces. The reported experimental results confirm the efficiency of the presented method.

Network Traffic Prediction and Anomaly Detection Based on ARFIMA Model

The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure (possibility of passive and active cyberattacks) are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cook’s distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brown’s, Holt’s, and Winters’ models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series.

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Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

In this paper we focus on DDoS attacks detection by means of greedy algorithms. In particular we propose to use Matching Pursuit and Orthogonal Matching Pursuit algorithms. The major contribution of the paper is the proposition of 1D KSVD algorithm as well as its tree based structure representation (clusters), that can be successfully applied to DDos attacks and network anomaly detection.

DDoS Attacks Detection by Means of Greedy Algorithms

In this paper we propose anomaly detection preprocessor for SNORT IDS Intrusion Detection System [1] base on probabilistic and signal processing algorithms working in parallel. Two different algorithms increasing probability of detecting anomalies in network traffic. 25 network traffic features were used by preprocessor for detecting anomalies. Preprocessor calculated Chi-square statistic test and energy from DWT Discrete Wavelet Transform subband coefficients. Usability of proposed SNORT extension was evaluated in local LAN network.

Łukasz Saganowski

Papers

Statistical and signal-based network traffic recognition for anomaly detection

Network Traffic Prediction and Anomaly Detection Based on ARFIMA Model

Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

DDoS Attacks Detection by Means of Greedy Algorithms

Anomaly Detection Preprocessor for SNORT IDS System