Detection of Non-Technical Losses in Smart Distribution Networks: a Review

With the advent of smart grids, distribution utilities have initiated a large deployment of smart meters on the premises of the consumers. The enormous amount of data obtained from the consumers and communicated to the utility give new perspectives and possibilities for various analytics-based applications. In this paper the current smart metering-based energy-theft detection schemes are reviewed and discussed according to two main distinctive categories: A) system statebased, and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT

Detection of Non-Technical Losses: The Project MIDAS

The MIDAS project began in 2006 as collaboration between Endesa, Sadiel, and the University of Seville. The objective of the MIDAS project is the detection of Non-Technical Losses (NTLs) on power utilities. The NTLs represent the non-billed energy due to faults or illegal manipulations in clients’ fa cilities. Initially, research lines study the application of techniques of data mining and neural networks. After several researches, the studies are expanded to other research fields: expert systems, text mining, statistical techniques, pattern recognition, etc. These techniques have provided an automated system for detection of NTLs on company databases. This system is in the test phase, and it is applied in real cases in company databases

Security assessment of the smart grid : a review focusing on the NAN architecture

Abstract: This paper presents a comprehensive review on the security aspect of the smart grid communication network. The paper focus on the Neighborhood Area Network (NAN) cybersecurity and it laid emphasis on how the NAN architecture is such an attractive target to intruders and attackers. The paper aims at summarizing recent research efforts on some of the attacks and the various techniques employed in tackling them as they were discussed in recent literatures and research works. Furthermore, the paper presents a detailed review on the smart grid communication layers, wireless technology standards, networks and the security challenges the grid is currently facing. The work concludes by explaining current and future directions NAN communication security could consider in terms of data privacy measures. The data privacy measures are discussed in terms of prevention and detection techniques

eChIDNA: Continuous Data Validation in Advanced Metering Infrastructures

New laws and regulations increase the demands for a more data-intense metering infrastructure towards more adaptive electricity networks (aka smart grids). The automatic measuring, often involving wireless communication, introduces errors both in software and during data transmission. These demands, as well as the large data volumes that need to be validated, present new challenges to utilities. First, measurement errors cannot be allowed to propagate to the data stored by utilities. Second, manual fixing of errors after storing is not a feasible option with increasing data volumes and decreasing lead times for new services and analysis. Third, validation is not only to be applied to current readings but also to past readings when new types of errors are discovered. This paper addresses these issues by proposing a hybrid system, eChIDNA, utilizing both the store-then-process and the data streaming processing paradigms, enabling for high throughput, low latency distributed and parallel analysis. Validation rules are built upon this paradigm and then implemented on the state of the art Apache Storm Stream Processing Engine to assess performance. Furthermore, patterns of common errors are matched, triggering alerts as a first step towards automatic correction of errors. The system is evaluated with production data from hundreds of thousands of smart meters. The results show a performance in the thousands messages per second realm, showing that stream processing can be used to validate large volumes of meter data online with low processing latency, identifying common errors as they appear. The results from the pattern matching are cross-validated with system experts and show that pattern matching is a viable way to minimize time required from human operators

Intrusion Detection in Industrial Networks via Data Streaming

Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in\ua0 Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations.In this chapter we explain that\ua0 stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to\ua0 facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems).We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment

Predictive Abuse Detection for a PLC Smart Lighting Network Based on Automatically Created Models of Exponential Smoothing

One of the basic elements of a Smart City is the urban infrastructure management system, in particular, systems of intelligent street lighting control. However, for their reliable operation, they require special care for the safety of their critical communication infrastructure. This article presents solutions for the detection of different kinds of abuses in network traffic of Smart Lighting infrastructure, realized by Power Line Communication technology. Both the structure of the examined Smart Lighting network and its elements are described. The article discusses the key security problems which have a direct impact on the correct performance of the Smart Lighting critical infrastructure. In order to detect an anomaly/attack, we proposed the usage of a statistical model to obtain forecasting intervals. Then, we calculated the value of the differences between the forecast in the estimated traffic model and its real variability so as to detect abnormal behavior (which may be symptomatic of an abuse attempt). Due to the possibility of appearance of significant fluctuations in the real network traffic, we proposed a procedure of statistical models update which is based on the criterion of interquartile spacing. The results obtained during the experiments confirmed the effectiveness of the presented misuse detection method

Development of smart grid testbed with low-cost hardware and software for cybersecurity research and education

Smart Grid, also known as the next generation of the power grid, is considered as a power infrastructure with advanced information and communication technologies (ICT) that will enhance the efficiency and reliability of power systems. For the essential benefits that come with Smart Grid, there are also security risks due to the complexity of advanced ICT utilized in the architecture of Smart Grid to interconnect a huge number of devices and subsystems. Cybersecurity is one of the emerging major threats in Smart Grid that needs to be considered as the attack surface increased. To prevent cyber-attacks, new techniques and methods need to be evaluated in a real-world environment or in a testbed. However, the costs for setting-up Smart Grid testbed is extensive. In this article, we focused on the development of a smart grid testbed with a low-cost hardware and software for cybersecurity research and education. As a case study, we evaluated the testbed with most common cyber-attack such as denial of service (DoS) attack. In addition, the testbed is a useful resource for cybersecurity research and education on different aspects of SCADA systems such as protocol implementation, and PLC programming