Biblio

Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%.

It has been recently proposed that the reactive power injection of distributed energy resources (DERs) can be used to regulate the voltage across the power distribution network, and simple distributed control laws have been recently developed in the literature for performing such distributed Volt/VAR control. However, enabling the reactive-power injection capability of DERs also opens the door for potential adversarial attacks. Specifically, the adversary can compromise a subset of the DERs and use their reactive power to disrupt the voltage profile across the distribution network. In this paper, we study the potential damage (in terms of the voltage disruption) of such adversarial attacks and how to mitigate the damage by controlling the allowable range of reactive power injection at each bus. Somewhat surprisingly and contrary to the intuition that the reactive power injection at legitimate buses should help mitigating the voltage disruption inflicted by the adversary, we demonstrate that an intelligent attacker can actually exploit the response of the legitimate buses to amplify the damage by two times. Such a higher level of damage can be attained even when the adversary has no information about the network topology. We then formulate an optimization problem to limit the potential damage of such adversarial attacks. Our formulation sets the range of the reactive power injection on each bus so that the damage by the adversary is minimized, subject to the constraint that the voltage mismatch (without attack) can still be maintained within a given threshold under an uncertainty set of external inputs. Numerical results demonstrate the validity of our analysis and the effectiveness of our approach to mitigate the damage caused by such attacks.

Climate change represents a serious threat to the health of our planet and imposed a discussion upon energy waste and production. In this paper we propose a smart grid architecture relying on blockchain technology aimed at discouraging the production and distribution of non-renewable energy as the one derived from fossil fuel. Our model relies on a reverse application of a recently introduced attack to the blockchain based on chain forking. Our system involves both a central authority and a number of distributed peers representing the stakeholders of the energy grid. This system preserves those advantages derived from the blockchain and it also address some limitations such as energy waste for mining operations. In addition, the reverse attack we rely on allows to mitigate the behavior of a classic blockchain, which is intrinsecally self-regulated, and to trigger a sort of ethical action which penalizes non-renewable energy producers. Blacklisted stakeholders will be induced to provide their transaction with higher fees in order to preserve the selling rate.

Compromised smart meters sending false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on the smart grid»s operation. Most existing defense models only deal with electricity theft from individual customers (isolated attacks) using supervised classification techniques that do not offer scalable or real time solutions. Furthermore, the cyber and interconnected nature of AMIs can also be exploited by organized adversaries who have the ability to orchestrate simultaneous data falsification attacks after compromising several meters, and also have more complex goals than just electricity theft. In this paper, we first propose a real time semi-supervised anomaly based consensus correction technique that detects the presence and type of smart meter data falsification, and then performs a consensus correction accordingly. Subsequently, we propose a semi-supervised consensus based trust scoring model, that is able to identify the smart meters injecting false data. The main contribution of the proposed approach is to provide a practical framework for compromised smart meter identification that (i) is not supervised (ii) enables quick identification (iii) scales classification error rates better for larger sized AMIs; (iv) counters threats from both isolated and orchestrated attacks; and (v) simultaneously works for a variety of data falsification types. Extensive experimental validation using two real datasets from USA and Ireland, demonstrates the ability of our proposed method to identify compromised meters in near real time across different datasets.

The advent of machine learning has made it a popular tool in various areas. It has also been applied in network intrusion detection. However, machine learning hasn't been sufficiently explored in the cyberphysical domains such as smart grids. This is because a lot of factors weigh in while using these tools. This paper is about intrusion detection in smart grids and how some machine learning techniques can help achieve this goal. It considers the problems of feature and classifier selection along with other data ambiguities. The goal is to apply the machine learning ensemble classifiers on the smart grid traffic and evaluate if these methods can detect anomalies in the system.

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.

Power grid infrastructures have been exposed to several terrorists and cyber attacks from different perspectives and have resulted in critical system failures. Among different attack strategies, simultaneous attack is feasible for the attacker if enough resources are available at the moment. In this paper, vulnerability analysis for simultaneous attack is investigated, using a modified cascading failure simulator with reduced calculation time than the existing methods. A new damage measurement matrix is proposed with the loss of generation power and time to reach the steady-state condition. The combination of attacks that can result in a total blackout in the shortest time are considered as the strongest simultaneous attack for the system from attacker's viewpoint. The proposed approach can be used for general power system test cases. In this paper, we conducted the experiments on W&W 6 bus system and IEEE 30 bus system for demonstration of the result. The modified simulator can automatically find the strongest attack combinations for reaching maximum damage in terms of generation power loss and time to reach black-out.

Security is an important requirement of every reactive system of the smart gird. The devices connected to the smart system in smart grid are exhaustively used to provide digital information to outside world. The security of such a system is an essential requirement. The most important component of such smart systems is Operating System (OS). This paper mainly focuses on the security of OS by incorporating Access Control Mechanism (ACM) which will improve the efficiency of the smart system. The formal methods use applied mathematics for modelling and analysing of smart systems. In the proposed work Formal Security Analysis (FSA) is used with model checking and hence it helped to prove the security of smart systems. When an Operating System (OS) takes into consideration, it never comes to a halt state. In the proposed work a Transition System (TS) is designed and the desired rules of security are provided by using Linear Temporal Logics (LTL). Unlike other propositional and predicate logic, LTL can model reactive systems with a prediction for the future state of the systems. In the proposed work, Simple Promela Interpreter (SPIN) is used as a model checker that takes LTL and TS of the system as input. Hence it is possible to derive the Büchi automaton from LTL logics and that provides traces of both successful and erroneous computations. Comparison of Büchi automaton with the transition behaviour of the OS will provide the details of security violation in the system. Validation of automaton operations on infinite computational sequences verify that whether systems are provably secure or not. Hence the proposed formal security analysis will provably ensures the security of smart systems in the area of smart grid applications.

Power system simulation environments with appropriate time-fidelity are needed to enable rapid testing of new smart grid technologies and for coupled simulations of the underlying cyber infrastructure. This paper presents such an environment which operates with power system models in the PMU time frame, including data visualization and interactive control action capabilities. The flexible and extensible capabilities are demonstrated by interfacing with a cyber infrastructure simulation.

Software Defined Networks (SDNs) is a new networking paradigm that has gained a lot of attention in recent years especially in implementing data center networks and in providing efficient security solutions. The popularity of SDN and its attractive security features suggest that it can be used in the context of smart grid systems to address many of the vulnerabilities and security problems facing such critical infrastructure systems. This paper studies the impact of different cyber attacks that can target smart grid communication network which is implemented as a software defined network on the operation of the smart grid system in general. In particular, we perform different attack scenarios including DDoS attacks, location highjacking and link overloading against SDN networks of different controller types that include POX, Floodlight and RYU. Our experiments were carried out using the mininet simulator. The experiments show that SDN-enabled smartgrid systems are vulnerable to different types of attacks.

With the rapid development of DC transmission technology and High Voltage Direct Current (HVDC) programs, the reliability of AC/DC hybrid power grid draws more and more attentions. The paper takes both the system static and dynamic characteristics into account, and proposes a novel AC/DC hybrid system reliability evaluation method considering transient security constraints based on Monte-Carlo method and transient stability analytical method. The interaction of AC system and DC system after fault is considered in evaluation process. The transient stability analysis is performed firstly when fault occurs in the system and BPA software is applied to the analysis to improve the computational accuracy and speed. Then the new system state is generated according to the transient analysis results. Then a minimum load shedding model of AC/DC hybrid system with HVDC is proposed. And then adequacy analysis is taken to the new state. The proposed method can evaluate the reliability of AC/DC hybrid grid more comprehensively and reduce the complexity of problem which is tested by IEEE-RTS 96 system and an actual large-scale system.

This paper presents a new approach for a dynamic curtailment method for renewable energy sources that guarantees fulfilling of (n-1)-security criteria of the system. Therefore, it is applicable to high voltage distribution grids and has compliance to their planning guidelines. The proposed dynamic curtailment method specifically reduces the power feed-in of renewable energy sources up to a level, where no thermal constraint is exceeded in the (n-1)-state of the system. Based on AC distribution factors, a new formulation of line outage distribution factors is presented that is applicable for outages consisting of a single line or multiple segment lines. The proposed method is tested using a planning study of a real German high voltage distribution grid. The results show that any thermal loading limits are exceeded by using the dynamic curtailment approach. Therefore, a significant reduction of the grid reinforcement can be achieved by using a small amount of curtailed annual energy from renewable energy sources.

the terms Smart grid, IntelliGrid, and secure astute grid are being used today to describe technologies that automatically and expeditiously (separate far from others) faults, renovate potency, monitor demand, and maintain and recuperate (firm and steady nature/lasting nature/vigor) for more reliable generation, transmission, and distribution of electric potency. In general, the terms describe the utilization of microprocessor-predicated astute electronic contrivances (IEDs) communicating with one another to consummate tasks afore now done by humans or left undone. These IEDs watch/ notice/ celebrate/ comply with the state of the puissance system, make edified decisions, and then take action to preserve the (firm and steady nature/lasting nature/vigor) and performance of the grid. Technology use/military accommodation in the home will sanction end users to manage their consumption predicated on their own predilections. In order to manage their consumption or the injuctive authorization placed on the grid, people (who utilize a product or accommodation) need information and an (able to transmute and get better) power distribution system. The astute grid is an accumulation of information sources and the automatic control system that manages the distribution of puissance, understands the transmutations in demand, and reacts to it by managing demand replication. Different billing (prosperity plans/ways of reaching goals) for mutable time and type of avail, as well as conservation and use or sale of distributed utilizable things/valuable supplies, will become part of perspicacious solutions. The traditional electrical power grid is currently evolving into the perspicacious grid. Perspicacious grid integrates the traditional electrical power grid with information and communication technologies (ICT). Such integration empowers the electrical utilities providers and consumers, amends the efficiency and the availability of the puissance system while perpetually monitoring, - ontrolling and managing the authoritative ordinances of customers. A keenly intellective grid is an astronomically immense intricate network composed of millions of contrivances and entities connected with each other. Such a massive network comes with many security concerns and susceptibilities. In this paper, we survey the latest on keenly intellective grid security. We highlight the involution of the keenly intellective grid network and discuss the susceptibilities concrete to this sizably voluminous heterogeneous network. We discuss then the challenges that subsist in securing the keenly intellective grid network and how the current security solutions applied for IT networks are not adequate to secure astute grid networks. We conclude by over viewing the current and needed security solutions for the keenly intellective gird.

Distribution system security region (DSSR) has been widely used to analyze the distribution system operation security. This paper innovatively defines the scale of DSSR, namely the number of boundary constraints and variables of all operational constraints, analyzes and puts forward the corresponding evaluation method. Firstly, the influence of the number of security boundary constraints and variables on the scale of DSSR is analyzed. The factors that mainly influence the scale are found, such as the number of transformers, feeders, as well as sectionalizing switches, and feeder contacts modes between transformers. Secondly, a matrix representing the relations among transformers in distribution system is defined to reflect the characteristics of network's structure, while an algorithm of the scale of DSSR based on transformers connection relationship matrix is proposed, which avoids the trouble of listing security region constraints. Finally, the proposed method is applied in a test system to confirm the effectiveness of the concepts and methods. It provides the necessary foundation for DSSR theory as well as safety analysis.

With the development of smart grid, information and energy integrate deeply. For remote monitoring and cluster management, SCADA system of wind farm should be connected to Internet. However, communication security and operation risk put forward a challenge to data network of the wind farm. To address this problem, an active security defense strategy combined whitelist and security situation assessment is proposed. Firstly, the whitelist is designed by analyzing the legitimate packet of Modbus on communication of SCADA servers and PLCs. Then Knowledge Automation is applied to establish the Decision Requirements Diagram (DRD) for wind farm security. The D-S evidence theory is adopted to assess operation situation of wind farm and it together with whitelist offer the security decision for wind turbine. This strategy helps to eliminate the wind farm owners' security concerns of data networking, and improves the integrity of the cyber security defense for wind farm.

One of the key objectives of distributed denial of service (DDoS) attack on the smart grid advanced metering infrastructure is to threaten the availability of end user's metering data. This will surely disrupt the smooth operations of the grid and third party operators who need this data for billing and other grid control purposes. In previous work, we proposed a cloud-based Openflow firewall for mitigation against DDoS attack in a smart grid AMI. In this paper, PRISM model checker is used to perform a probabilistic best-and worst-case analysis of the firewall with regard to DDoS attack success under different firewall detection probabilities ranging from zero to 1. The results from this quantitative analysis can be useful in determining the extent the DDoS attack can undermine the correctness and performance of the firewall. In addition, the study can also be helpful in knowing the extent the firewall can be improved by applying the knowledge derived from the worst-case performance of the firewall.

There has been a growing spate of Cyber attacks targeted at different corporate enterprises and systems across the globe. The scope of these attacks spans from small scale (grid and control system manipulation, domestic meter cyber hacking etc) to large scale distributed denial of service attacks (DDoSA) in enterprise networks. The effect of hacking on control systems through distributed control systems (DCS) using communication protocols on vulnerable home area networks (HANs) and neighborhood area networks (NANs) is terrifying. To meet the current security requirements, a new security network is proposed called Smart grid convoluted network (SGCN). With SGCN, the basic activities of data processing, monitoring and query requests are implemented outside the grid using Fog computing layer-3 devices (gatekeepers). A cyber monitor agent that leverages a reliable end-to end-communication network to secure the systems components on the grid is employed. Cyber attacks which affects the computational requirements of SG applications is mitigated by using a Fourier predictive cyber monitor (FPCM). The network uses flexible resources with loopback services shared across the network. Serial parallelism and efficient bandwidth provisioning are used by the locally supported Fog nodes within the SG cloud space. For services differentiation, SGCN employed secure communication between its various micro-grids as well as its metering front-ends. With the simulated traffic payload extraction trend (STPET), SGCN promises hard time for hackers and malicious malwares. While the work guarantees security for SGs, reliability is still an open issue due to the complexity of SG architecture. In conclusion, the future of the Cyber security in SGs must employ the concept of Internet of Everything (IoE), Malware predictive analytics and Fog layers on existing SG prototypes for optimal security benefits.

In this paper, we focus on the security issues and challenges in smart grid. Smart grid security features must address not only the expected deliberate attacks, but also inadvertent compromises of the information infrastructure due to user errors, equipment failures, and natural disasters. An important component of smart grid is the advanced metering infrastructure which is critical to support two-way communication of real time information for better electricity generation, distribution and consumption. These reasons makes security a prominent factor of importance to AMI. In recent times, attacks on smart grid have been modelled using attack tree. Attack tree has been extensively used as an efficient and effective tool to model security threats and vulnerabilities in systems where the ultimate goal of an attacker can be divided into a set of multiple concrete or atomic sub-goals. The sub-goals are related to each other as either AND-siblings or OR-siblings, which essentially depicts whether some or all of the sub-goals must be attained for the attacker to reach the goal. On the other hand, as a security professional one needs to find out the most effective way to address the security issues in the system under consideration. It is imperative to assume that each attack prevention strategy incurs some cost and the utility company would always look to minimize the same. We present a cost-effective mechanism to identify minimum number of potential atomic attacks in an attack tree.

Non-Technical Loss (NTL) fraud is a very common fraud in power systems. In traditional power grid, energy theft, via meter tampering, is the main form of NTL fraud. With the rise of Smart Grid, adversaries can take advantage of two-way communication to commit NTL frauds by meter manipulation or network intrusion. Previous schemes were proposed to detect NTL frauds but are not efficient. In this paper, we propose a Fast NTL Fraud Detection and verification scheme (FNFD). FNFD is based on Recursive Least Square (RLS) to model adversary behavior. Experimental results show that FNFD outperforms existing schemes in terms of efficiency and overhead.

Security is a major challenge preventing wide deployment of the smart grid technology. Typically, the classical power grid is protected with a set of isolated security tools applied to individual grid components and layers ignoring their cross-layer interaction. Such an approach does not address the smart grid security requirements because usually intricate attacks are cross-layer exploiting multiple vulnerabilities at various grid layers and domains. We advance a conceptual layering model of the smart grid and a high-level overview of a security framework, termed CyNetPhy, towards enabling cross-layer security of the smart grid. CyNetPhy tightly integrates and coordinates between three interrelated, and highly cooperative real-time security systems crossing section various layers of the grid cyber and physical domains to simultaneously address the grid's operational and security requirements. In this article, we present in detail the physical security layer (PSL) in CyNetPhy. We describe an attack scenario raising the emerging hardware Trojan threat in process control systems (PCSes) and its novel PSL resolution leveraging the model predictive control principles. Initial simulation results illustrate the feasibility and effectiveness of the PSL.
 

Smart Grid is the trend of next generation power distribution and network management that enable a two -- way interactive communication and operation between consumers and suppliers, so as to achieve intelligent resource management and optimization. The wireless mesh network technology is a promising infrastructure solution to support these smart functionalities, while it has some inherent vulnerabilities and cyber-attack risks to be addressed. As Smart Grid is heavily relying on the underlie communication networks, which makes their security and dependability issues critical to the entire smart grid technology. Several studies have been conducted in the field of Smart Grid security, but few works were focused on the dependability and its associated resource analysis of the control center networks. In this paper, we have investigated the dependability modeling and also resource allocation in redundant communication networks by adopting two mathematical approaches, Reliability Block Diagrams (RBD) and Stochastic Petri Nets (SPNs), to analyze the dependability of control center networks in Smart Grid environment. We have applied our proposed modeling approach in an extensive case study to evaluate the availability of smart gird networks with different redundancy mechanisms. A combination of dependability models and reliability importance are used to analyze the network availability according to the most important components. We also show the variation of network availability in accordance with Mean Time to Failure (MTTF) in different network architectures.