Biblio

The evolution of power system as a smart grid (SG) not only has enhanced the monitoring and control capabilities of the power grid, but also raised its security concerns and vulnerabilities. With a boom in Internet of Things (IoT), a lot a sensors are being deployed across the grid. This has resulted in huge amount of data available for processing and analysis. Machine learning (ML) and deep learning (DL) algorithms are being widely used to extract useful information from this data. In this context, this paper presents a comprehensive literature survey of different ML and DL techniques that have been used in the smart grid cyber security area. The survey summarizes different type of cyber threats which today's SGs are prone to, followed by various ML and DL-assisted defense strategies. The effectiveness of the ML based methods in enhancing the cyber security of SGs is also demonstrated with the help of a case study.

This paper presents a hardware-in-the-loop cyber-physical system architecture design to monitor and control smart lights connected to the active distribution grid. The architecture uses Zigbee-based (IEEE 802.15.4) wireless sensor networks and publish-subscribe architecture to exchange monitoring and control signals between smart-light actuators (SLAs) and a smart-light central controller (SLCC). Each SLA integrated into a smart light consists of a Zigbee-based endpoint module to send and receive signals to and from the SLCC. The SLCC consists of a Zigbee-based coordinator module, which further exchanges the monitoring and control signals with the active distribution management system over the TCP/IP communication network. The monitoring signals from the SLAs include light status, brightness level, voltage, current, and power data, whereas, the control signals to the SLAs include light intensity, turn ON, turn OFF, standby, and default settings. We have used our existing hardware-in-the-loop (HIL) cyber-physical system (CPS) security SCADA testbed to process signals received from the SLCC and respond suitable control signals based on the smart light schedule requirements, system operation, and active distribution grid dynamic characteristics. We have integrated the proposed cyber-physical smart light control system (CPSLCS) testbed to our existing HIL CPS SCADA testbed. We use the integrated testbed to demonstrate the efficacy of the proposed algorithm by real-time performance and latency between the SLCC and SLAs. The experiments demonstrated significant results by 100% realtime performance and low latency while exchanging data between the SLCC and SLAs.

The generation, transmission, distribution, and storage of electric power is becoming increasingly decentralized. Advances in Distributed Energy Resources (DERs) are rapidly changing the nature of the power grid. Moreover, the accommodation of these new technologies by the legacy grid requires that an increasing number of devices be Internet connected so as to allow for sensor and actuator information to be collected, transmitted, and processed. With the wide adoption of the Internet of Things (IoT), the cybersecurity vulnerabilities of smart grid devices that can potentially affect the stability, reliability, and resilience of the power grid need to be carefully examined and addressed. This is especially true in situations in which smart grid devices are deployed with default configurations or without reasonable protections against malicious activities. While much work has been done to characterize the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices, this paper demonstrates that similar vulnerabilities associated with the newer class of IoT smart grid devices are becoming a concern. Specifically, this paper first performs an evaluation of such devices using the Shodan platform and text processing techniques to analyze a potential vulnerability involving the lack of password protection. This work further explores several Shodan search terms that can be used to identify additional smart grid components that can be evaluated in terms of cybersecurity vulnerabilities. Finally, this paper presents recommendations for the more secure deployment of such smart grid devices.

Nowadays, Internet Of Everything (IOE) has demanded for a wide range of applications areas. IOE is started to replaces an Internet Of things (IOT). IOE is a combination of massive number of computing elements and sensors, people, processes and data through the Internet infrastructure. Device to Device communication and interfacing of Wireless Sensor network with IOE can makes any system as a Smart System. With the increased the use of Internet and Internet connected devices has opportunities for hackers to launch attacks on unprecedented scale and impact. The IOE can serve the varied security in the various sectors like manufacturing, agriculture, smart grid, payments, IoT gateways, healthcare and industrial ecosystems. To secure connections among people, process, data, and things, is a major challenge in Internet of Everything.. This paper focuses on various security Issues and Authentication Schemes in the IOE systems.

The purpose of Complex Networks Monitoring and Security and Fraud Detection for Enterprises - CoNeSec track is two-fold: Firstly, the track offers a forum for scientists and engineers to exchange ideas on novel analytical techniques using network log data. Secondly, the track has a thematic focus on emerging technology for complex network, security and privacy. We seek publications on all theoretical and practical work in areas related to the theme above.

Internet of things is a network formed between two or more devices through internet which helps in sharing data and resources. IoT is present everywhere and lot of applications in our day-to-day life such as smart homes, smart grid system which helps in reducing energy consumption, smart garbage collection to make cities clean, smart cities etc. It has some limitations too such as concerns of security of the network and the cost of installations of the devices. There have been many researches proposed various method in improving the IoT systems. In this paper, we have discussed about the scope and limitations of IoT in various fields and we have also proposed a technique to secure offline architecture of IoT.

This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid's transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system's operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator's defense cost.

This paper focuses on the secure integration of distributed energy resources (DERs), especially pluggable electric vehicles (EVs), with the power grid. We consider the vehicle-to-grid (V2G) system where EVs are connected to the power grid through an `aggregator' In this paper, we propose a novel Cyber-Physical Anomaly Detection Engine that monitors system behavior and detects anomalies almost instantaneously (worst case inspection time for a packet is 0.165 seconds1). This detection engine ensures that the critical power grid component (viz., aggregator) remains secure by monitoring (a) cyber messages for various state changes and data constraints along with (b) power data on the V2G cyber network using power measurements from sensors on the physical/power distribution network. Since the V2G system is time-sensitive, the anomaly detection engine also monitors the timing requirements of the protocol messages to enhance the safety of the aggregator. To the best of our knowledge, this is the first piece of work that combines (a) the EV charging/discharging protocols, the (b) cyber network and (c) power measurements from physical network to detect intrusions in the EV to power grid system.1Minimum latency on V2G network is 2 seconds.

Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.

The ongoing digitalization of the power distribution grid will improve the operational support and automation which is believed to increase the system reliability. However, in an integrated and interdependent cyber-physical system, new threats appear which must be understood and dealt with. Of particular concern, in this paper, is the causes of an inconsistent view between the physical system (here power grid) and the Information and Communication Technology (ICT) system (here Distribution Management System). In this paper we align the taxonomy used in International Electrotechnical Commission (power eng.) and International Federation for Information Processing (ICT community), define a metric for inconsistencies, and present a modelling approach using Stochastic Activity Networks to assess the consequences of inconsistencies. The feasibility of the approach is demonstrated in a simple use case.

The tremendous advances in information and communications technology (ICT), as well as the embedded systems, have been led to the emergence of the novel concept of the internet of things (IoT). Enjoying IoT-based technologies, many objects and components can be connected to each other through the internet or other modern communicational platforms. Embedded systems which are computing machines for special purposes like those utilized in high-tech devices, smart buildings, aircraft, and vehicles including advanced controllers, sensors, and meters with the ability of information exchange using IT infrastructures. The phrase "internet", in this context, does not exclusively refer to the World Wide Web rather than any type of server-based or peer-to-peer networks. In this study, the application of IoT in smart grids is addressed. Hence, at first, an introduction to the necessity of deployment of IoT in smart grids is presented. Afterwards, the applications of IoT in three levels of generation, transmission, and distribution is proposed. The generation level is composed of applications of IoT in renewable energy resources, wind and solar in particular, thermal generation, and energy storage facilities. The deployment of IoT in transmission level deals with congestion management in power system and guarantees the security of the system. In the distribution level, the implications of IoT in active distribution networks, smart cities, microgrids, smart buildings, and industrial sector are evaluated.

Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection (FDI) attacks that can bypass bad data detection mechanisms. Existing mitigation in the power system either focus on redundant measurements or protect a set of basic measurements. These methods make specific assumptions about FDI attacks, which are often restrictive and inadequate to deal with modern cyber threats. In the proposed approach, a deep learning based framework is used to detect injected data measurement. Our time-series anomaly detector adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. To effectively estimate system variables, our approach observes both data measurements and network level features to jointly learn system states. The proposed system is tested on IEEE 39-bus system. Experimental analysis shows that the deep learning algorithm can identify anomalies which cannot be detected by traditional state estimation bad data detection.

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.

Smart grid consists of smart devices to control, record and analyze the grid power flow. All these devices belong to the latest technology, which is used to interact through the wireless network making the grid communication network vulnerable to cyber attack. This paper deals with a novel approach using altering the Internet Protocol (IP) address of the smart grid communication network using fuzzy logic according to the degree of node. Through graph theory approach Wireless Communication Network (WCN) is designed by considering each node of the system as a smart sensor. In this each node communicates with other nearby nodes for exchange of data. Whenever there is cyber intrusion the WCN change its IP using proposed fuzzy rules, where higher degree nodes are given the preference to change first with extreme IP available in the system. Using the proposed algorithm, different IEEE test systems are simulated and compared with existing Dynamic Host Configuration Protocol (DHCP). The fuzzy logic approach reduces the data loss and improves the system response time.

A Cyber Physical Sensor System (CPSS) consists of a computing platform equipped with wireless access points, sensors, and actuators. In a Cyber Physical System, CPSS constantly collects data from a physical object that is under process and performs local real-time control activities based on the process algorithm. The collected data is then transmitted through the network layer to the enterprise command and control center or to the cloud computing services for further processing and analysis. This paper investigates the CPSS' most common cyber security threats and vulnerabilities and provides countermeasures. Furthermore, the paper addresses how the CPSS are attacked, what are the leading consequences of the attacks, and the possible remedies to prevent them. Detailed case studies are presented to help the readers understand the CPSS threats, vulnerabilities, and possible solutions.

The existing radial topology makes the power system less reliable since any part in the system failure will disrupt electrical power delivery in the network. The increasing security concerns, electrical energy theft, and present advancement in Information and Communication Technologies are some factors that led to modernization of power system. In a smart grid, a network of smart sensors offers numerous opportunities that may include monitoring of power, consumer-side energy management, synchronization of dispersed power storage, and integrating sources of renewable energy. Smart sensor networks are low cost and are ease to deploy hence they are favorable contestants for deployment smart power grids at a larger scale. These networks will result in a colossal volume of dissimilar range of data that require an efficient processing and analyzing process in order to realize an efficient smart grid. The existing technology can be used to collect data but dealing with the collected information proficiently as well as mining valuable material out of it remains challenging. The paper investigates communication technologies that maybe deployed in a smart grid. In this paper simulations results for the Additive White Gaussian Noise (AWGN) channel are illustrated. We propose a model and a communication network domain riding on the power system domain. The model was interrogated by simulation in MATLAB.

The power outages of the last couple of years around the world introduce the indispensability of technological development to improve the traditional power grids. Early warnings of imminent failures represent one of the major required improvements. Costly blackouts throughout the world caused by the different severe incidents in traditional power grids have motivated researchers to diagnose and investigate previous blackouts and propose a prediction model that enables to prevent power outages. Although, in the new generation of power grid, the smart grid's (SG) real time data can be used from smart meters (SMs) and phasor measurement unit sensors (PMU) to prevent blackout, it demands high reliability and stability against power outages. This paper implements a proactive prediction model based on deep-belief networks that can predict imminent blackout. The proposed model is evaluated on a real smart grid dataset. Promising results are reported in the case study.

For the operation of electrical distribution system an increased shift towards smart grid operation can be observed. This shift provides operators with a high level of reliability and efficiency when dealing with highly dynamic distribution grids. Technically, this implies that the support for a bidirectional flow of data is critical to realizing smart grid operation, culminating in the demand for equipping grid entities (such as sensors) with communication and processing capabilities. Unfortunately, the retrofitting of brown-field electric substations in distribution grids with these capabilities is not straightforward - this scenario requires a solution that provides "industry-grade" Internet of Things capabilities at "consumer-grade" prices (e.g., off-the-shelf communication standards and hardware). In this paper, we discuss the particular challenge of precisely time-synchronized wireless data collection in secondary substations that at the same time supports on-site configuration by authorized maintenance personnel through a mobile application: to achieve this, we propose a combined implementation of IPv6 over Bluetooth Low Energy.

Smart grids require communication networks for supervision functions and control operations. With this they become attractive targets for attackers. In newer power grids, State Estimation (SE) is often performed based on Kalman Filters (KFs) to deal with noisy measurement data and detect Bad Data (BD) due to failures in the measurement system. Nevertheless, in a setting where attackers can gain access to modify sensor data, they can exploit the fact that SE is used to process the data. In this paper, we show how an attacker can modify Phasor Measurement Unit (PMU) sensor data in a way that it remains undetected in the state estimation process. We show how anomaly detection methods based on innovation gain fail if an attacker is aware of the state estimation and uses the right strategy to circumvent detection.

With Electricity as a fundamental part of our life, its production has still large, negative environmental impact. Therefore, one strain of research is to optimize electricity usage by avoiding its unnecessary consumption or time its consumption when green energy is available. The shift towards an Advanced Metering Infrastructure (AMI) allows to optimize energy distribution based on the current load at residence level. However, applications such as Demand Management and Advanced Load Forecasting require information further down at device level, which cannot be provided by standard electricity meters nor existing AMIs. Hence, different approaches for appliance monitoring emerged over the past 30 years which are categorized into Intrusive systems requiring multiple distributed sensors and Non-Intrusive systems requiring a single unobtrusive sensor. Although each category has been individually explored, hybrid approaches have received little attention. Our experiments highlight that variable consumer devices (e.g. PCs) are detrimental to the detection performance of non-intrusive systems. We further show that their influence can be inhibited by using sensor data from additional intrusive sensors. Even fairly straightforward sensor fusion techniques lead to a classification performance (F1) gain from 84.88 % to 93.41 % in our test setup. As this highlights the potential to contribute to the global goal of saving energy, we define further research directions for hybrid load monitoring systems.

Nowadays, electricity companies have started applying smart grid in their systems rather than the conventional electrical grid (manual grid). Smart grid produces an efficient and effective energy management and control, reduces the cost of production, saves energy and it is more reliable compared to the conventional grid. As an advanced energy meter, smart meters can measure the power consumption as well as monitor and control electrical devices. Smart meters have been adopted in many countries since the 2000s as they provide economic, social and environmental benefits for multiple stakeholders. The design of smart meter can be customized depending on the customer and the utility company needs. There are different sensors and devices supported by dedicated communication infrastructure which can be utilized to implement smart meters. This paper presents a study of the challenges associated with smart meters, smart homes and smart grids as an effort to highlight opportunities for emerging research and industrial solutions.

Electronic power grid is a distributed network used for transferring electricity and power from power plants to consumers. Based on sensor readings and control system signals, power grid states are measured and estimated. As a result, most conventional attacks, such as denial-of-service attacks and random attacks, could be found by using the Kalman filter. However, false data injection attacks are designed against state estimation models. Currently, distributed Kalman filtering is proved effective in sensor networks for detection and estimation problems. Since meters are distributed in smart power grids, distributed estimation models can be used. Thus in this paper, we propose a diffusion Kalman filter for the power grid to have a good performance in estimating models and to effectively detect false data injection attacks.

Wireless Sensor Networks (WSNs) have been widely adopted to monitor various ambient conditions including critical infrastructures. Since power grid is considered as a critical infrastructure, and the smart grid has appeared as a viable technology to introduce more reliability, efficiency, controllability, and safety to the traditional power grid, WSNs have been envisioned as potential tools to monitor the smart grid. The motivation behind smart grid monitoring is to improve its emergency preparedness and resilience. Despite their effectiveness in monitoring critical infrastructures, WSNs also introduce various security vulnerabilities due to their open nature and unreliable wireless links. In this paper, we focus on the, Black-Hole (B-H) attack. To cope with this, we propose a hierarchical trust-based WSN monitoring model for the smart grid equipment in order to detect the B-H attacks. Malicious nodes have been detected by testing the trade-off between trust and dropped packet ratios for each Cluster Head (CH). We select different thresholds for the Packets Dropped Ratio (PDR) in order to test the network behaviour with them. We set four different thresholds (20%, 30%, 40%, and 50%). Threshold of 50% has been shown to reach the system stability in early periods with the least number of re-clustering operations.

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler (KL) divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.

Wireless Sensor Network is the combination of small devices called sensor nodes, gateways and software. These nodes use wireless medium for transmission and are capable to sense and transmit the data to other nodes. Generally, WSN composed of two types of nodes i.e. generic nodes and gateway nodes. Generic nodes having the ability to sense while gateway nodes are used to route that information. IoT now extended to IoET (internet of Everything) to cover all electronics exist around, like a body sensor networks, VANET's, smart grid stations, smartphone, PDA's, autonomous cars, refrigerators and smart toasters that can communicate and share information using existing network technologies. The sensor nodes in WSN have very limited transmission range as well as limited processing speed, storage capacities and low battery power. Despite a wide range of applications using WSN, its resource constrained nature given birth to a number severe security attacks e.g. Selective Forwarding attack, Jamming-attack, Sinkhole attack, Wormhole attack, Sybil attack, hello Flood attacks, Grey Hole, and the most dangerous BlackHole Attacks. Attackers can easily exploit these vulnerabilities to compromise the WSN network.