Biblio

This paper presents a review on how to benefit from software-defined networking (SDN) to enhance smart grid security. For this purpose, the attacks threatening traditional smart grid systems are classified according to availability, integrity, and confidentiality, which are the main cyber-security objectives. The traditional smart grid architecture is redefined with SDN and a conceptual model for SDN-based smart grid systems is proposed. SDN based solutions to the mentioned security threats are also classified and evaluated. Our conclusions suggest that SDN helps to improve smart grid security by providing real-time monitoring, programmability, wide-area security management, fast recovery from failures, distributed security and smart decision making based on big data analytics.

Smart grid utilizes cloud service to realize reliable, efficient, secured, and cost-effective power management, but there are a number of security risks in the cloud service of smart grid. The security risks are particularly problematic to operators of power information infrastructure who want to leverage the benefits of cloud. In this paper, security risk of cloud service in the smart grid are categorized and analyzed characteristics, and multi-layered index system of general technical risks is established, which applies to different patterns of cloud service. Cloud service risk of smart grid can evaluate according indexes.

The rapid evolution of the power grid into a smart one calls for innovative and compelling means to experiment with the upcoming expansions, and analyze their behavioral response under normal circumstances and when targeted by attacks. Such analysis is fundamental to setting up solid foundations for the smart grid. Smart grid Hardware-In-the-Loop (HIL) co-simulation environments serve as a key approach to answer questions on the systems components, functionality, security concerns along with analysis of the system outcome and expected behavior. In this paper, we introduce a HIL co-simulation framework capable of simulating the smart grid actions and responses to attacks targeting its power and communication components. Our testbed is equipped with a real-time power grid simulator, and an associated OpenStack-based communication network. Through the utilized communication network, we can emulate a multitude of attacks targeting the power system, and evaluating the grid response to those attacks. Moreover, we present different illustrative cyber attacks use cases, and analyze the smart grid behavior in the presence of those attacks.

The communication security issue brought by Smart Grid is of great importance and should not be ignored in backbone optical networks. With the aim to solve this problem, this paper firstly conducts deep analysis into the security challenge of optical network under smart power grid environment and proposes a so-called lightweight security signaling mechanism of multi-domain optical network for Energy Internet. The proposed scheme makes full advantage of current signaling protocol with some necessary extensions and security improvement. Thus, this lightweight security signaling protocol is designed to make sure the end-to-end trusted connection. Under the multi-domain communication services of smart power grid, evaluation simulation for the signaling interaction is conducted. Simulation results show that this proposed approach can greatly improve the security level of large-scale multi-domain optical network for smart power grid with better performance in term of connection success rate performance.

This paper proposes a game-theoretic approach to analyze the interactions between an attacker and a defender in a cyber-physical system (CPS) and develops effective defense strategies. In a CPS, the attacker launches cyber attacks on a number of nodes in the cyber layer, trying to maximize the potential damage to the underlying physical system while the system operator seeks to defend several nodes in the cyber layer to minimize the physical damage. Given that CPS attacking and defending is often a continual process, a zero-sum Markov game is proposed in this paper to model these interactions subject to underlying uncertainties of real-world events and actions. A novel model is also proposed in this paper to characterize the interdependence between the cyber layer and the physical layer in a CPS and quantify the impact of the cyber attack on the physical damage in the proposed game. To find the Nash equilibrium of the Markov game, we design an efficient algorithm based on value iteration. The proposed general approach is then applied to study the wide-area monitoring and protection issue in smart grid. Extensive simulations are conducted based on real-world data, and results show the effectiveness of the defending strategies derived from the proposed approach.

With the rapid development of the smart grid, a large number of intelligent sensors and meters have been introduced in distribution network, which will inevitably increase the integration of physical networks and cyber networks, and bring potential security threats to the operating system. In this paper, the functions of the information system on distribution network are described when cyber attacks appear at the intelligent electronic devices (lED) or at the distribution main station. The effect analysis of the distribution network under normal operating condition or in the fault recovery process is carried out, and the reliability assessment model of the distribution network considering cyber attacks is constructed. Finally, the IEEE-33-bus distribution system is taken as a test system to presented the evaluation process based on the proposed model.

In this paper, we present a work-in-progress approach to detect integrity attacks to Smart Grids by analyzing the readings from smart meters. Our approach is based on process mining and time-evolving graphs. In particular, process mining is used to discover graphs, from the dataset collecting the readings over a time period, that represent the behaviour of a customer. The time-evolving graphs are then compared in order to detect anomalous behavior of a customer. To evaluate the feasibility of our approach, we have conducted preliminary experiments by using the dataset provided by the Ireland's Commission for Energy Regulation (CER).

Reliable operation of power systems is a primary challenge for the system operators. With the advancement in technology and grid automation, power systems are becoming more vulnerable to cyber-attacks. The main goal of adversaries is to take advantage of these vulnerabilities and destabilize the system. This paper describes a game-theoretic approach to attacker / defender modeling in power systems. In our models, the attacker can strategically identify the subset of substations that maximize damage when compromised. However, the defender can identify the critical subset of substations to protect in order to minimize the damage when an attacker launches a cyber-attack. The algorithms for these models are applied to the standard IEEE-14, 39, and 57 bus examples to identify the critical set of substations given an attacker and a defender budget.

Cyber-Physical Systems (CPS), such as Water Distribution Networks (WDNs), deploy digital devices to monitor and control the behavior of physical processes. These digital devices, however, are susceptible to cyber and physical attacks, that may alter their functionality, and therefore the integrity of their measurements/actions. In practice, industrial control systems utilize simple control laws, which rely on various sensor measurements and algorithms which are expected to operate normally. To reduce the impact of a potential failure, operators may deploy redundant components; this however may not be useful, e.g., when a cyber attack at a PLC component occurs. In this work, we address the problem of reducing vulnerability to cyber-physical attacks in water distribution networks. This is achieved by augmenting the graph which describes the information flow from sensors to actuators, by adding new connections and algorithms, to increase the number of redundant cyber components. These, in turn, increase the \textitcyber-physical security level, which is defined in the present paper as the number of malicious attacks a CPS may sustain before becoming unable to satisfy the control requirements. A proof-of-concept of the approach is demonstrated over a simple WDN, with intuition on how this can be used to increase the cyber-physical security level of the system.

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.

Traditional security measures for large-scale critical infrastructure systems have focused on keeping adversaries out of the system. As the Internet of Things (IoT) extends into millions of homes, with tens or hundreds of devices each, the threat landscape is complicated. IoT devices have unknown access capabilities with unknown reach into other systems. This paper presents ongoing work on how techniques in sensor verification and cyber-physical modeling and analysis on bulk power systems can be applied to identify malevolent IoT devices and secure smart and connected communities against the most impactful threats.

At present, mobile terminals are widely used in power system and easy to be the target or springboard to attack the power system. It is necessary to have security assessment of power mobile terminal system to enable early warning of potential risks. In the context, this paper builds the security assessment system against to power mobile terminals, with features from security assessment system of general mobile terminals and power application scenarios. Compared with the existing methods, this paper introduces machine learning to the Rank Correlation Analysis method, which relies on expert experience, and uses objective experimental data to optimize the weight parameters of the indicators. From experiments, this paper proves that weights self-learning method can be used to evaluate the security of power mobile terminal system and improve credibility of the result.

The performance evaluation of distribution network operators is essential for the electrical utilities to know how prepared the operators are to execute their operation standards and rules, searching for minimizing the time of power outage, after some contingency. The performance of operators can be evaluated by the impact of their actions on several technical and economic indicators of the distribution system. This issue is a complex problem, whose solution involves necessarily some expertise and a multi-criteria evaluation. This paper presents a Tutorial Expert System (TES) for performance evaluation of electrical distribution network operators after a given contingency in the electrical network. The proposed TES guides the evaluation process, taking into account technical, economic and personal criteria, aiding the quantification of these criteria. A case study based on real data demonstrates the applicability of the performance evaluation procedure of distribution network operators.

Comparing with the traditional grid, energy internet will collect data widely and connect more broader. The analysis of electrical data use of Non-intrusive Load Monitoring (NILM) can infer user behavior privacy. Consideration both data security and availability is a problem must be addressed. Due to its rigid and provable privacy guarantee, Differential Privacy has proverbially reached and applied to privacy preserving data release and data mining. Because of its high sensitivity, increases the noise directly will led to data unavailable. In this paper, we propose a differentially private mechanism to protect energy internet privacy. Our focus is the aggregated data be released by data owner after added noise in disaggregated data. The theoretically proves and experiments show that our scheme can achieve the purpose of privacy-preserving and data availability.

False data injection attacks (FDIA) on state estimators are a kind of imminent cyber-physical security issue. Fortunately, it has been proved that if a set of measurements is strategically selected and protected, no FDIA will remain undetectable. In this paper, the metric Return on Investment (ROI) is introduced to evaluate the overall returns of the alternative measurement protection schemes (MPS). By setting maximum total ROI as the optimization objective, the previously ignored cost-benefit issue is taken into account to derive a realistic MPS for power utilities. The optimization problem is transformed into the Steiner tree problem in graph theory, where a tree pruning based algorithm is used to reduce the computational complexity and find a quasi-optimal solution with acceptable approximations. The correctness and efficiency of the algorithm are verified by case studies.

The collection of high frequency metering data in the emerging smart grid gives rise to the concern of consumer privacy. Anonymization of metering data is one of the proposed approaches in the literature, which enables transmission of unmasked data while preserving the privacy of the sender. Distributed anonymization methods can reduce the dependency on service providers, thus promising more privacy for the consumers. However, the distributed communication among the end-users introduces overhead and requires methods to prevent external attacks. In this paper, we propose four variants of a distributed anonymization method for smart metering data privacy, referred to as the Collaborative Anonymity Set Formation (CASF) method. The performance overhead analysis and security analysis of the variants are done using NS-3 simulator and the Scyther tool, respectively. It is shown that the proposed scheme enhances the privacy preservation functionality of an existing anonymization scheme, while being robust against external attacks.

With the rapid development of smart grid, smart meters are deployed at energy consumers' premises to collect real-time usage data. Although such a communication model can help the control center of the energy producer to improve the efficiency and reliability of electricity delivery, it also leads to some security issues. For example, this real-time data involves the customers' privacy. Attackers may violate the privacy for house breaking, or they may tamper with the transmitted data for their own benefits. For this purpose, many data aggregation schemes are proposed for privacy preservation. However, rare of them cares about both the data aggregation and fine-grained access control to improve the data utility. In this paper, we proposes a data aggregation scheme based on attribute decision tree. Security analysis illustrates that our scheme can achieve the data integrity, data privacy preservation and fine- grained data access control. Experiment results show that our scheme are more efficient than existing schemes.

The factors that threaten electric power information network are analyzed. Aiming at the weakness of being unable to provide numerical value of risk, this paper presents the evaluation index system, the evaluation model and method of network security based on multilevel fuzzy comprehensive judgment. The steps and method of security evaluation by the synthesis evaluation model are provided. The results show that this method is effective to evaluate the risk of electric power information network.

With the integration of computing, communication, and physical processes, the modern power grid is becoming a large and complex cyber physical power system (CPPS). This trend is intended to modernize and improve the efficiency of the power grid, yet it makes the CPPS vulnerable to potential cascading failures caused by cyber-attacks, e.g., the attacks that are originated by the cyber network of CPPS. To prevent these risks, it is essential to analyze how cyber-attacks can be conducted against the CPPS and how they can affect the power systems. In light of that General Packet Radio Service (GPRS) has been widely used in CPPS, this paper provides a case study by examining possible cyber-attacks against the cyber-physical power systems with GPRS-based SCADA system. We analyze the vulnerabilities of GPRS-based SCADA systems and focus on DoS attacks and message spoofing attacks. Furthermore, we show the consequence of these attacks against power systems by a simulation using the IEEE 9-node system, and the results show the validity of cascading failures propagated through the systems under our proposed attacks.

A smart grid is a fully automated power electricity network, which operates, protects and controls all its physical environments of power electricity infrastructure being able to supply energy in an efficient and reliable way. As the importance of cyber-physical system (CPS) security is growing, various vulnerability analysis methodologies for general systems have been suggested, whereas there has been few practical research targeting the smart grid infrastructure. In this paper, we highlight the significance of security vulnerability analysis in the smart grid environment. Then we introduce various automated vulnerability analysis techniques from executable files. In our approach, we propose a novel binary-based vulnerability discovery method for AMI and EV charging system to automatically extract security-related features from the embedded software. Finally, we present the test result of vulnerability discovery applied for AMI and EV charging system in Korean smart grid environment.

The previous consideration of power grid focuses on the power system itself, however, the recent work is aiming at both power grid and communication network, this coupling networks are firstly called as interdependent networks. Prior study on modeling interdependent networks always extracts main features from real networks, the model of network A and network B are completely symmetrical, both degree distribution in intranetwork and support pattern in inter-network, but in reality this circumstance is hard to attain. In this paper, we deliberately set both networks with same topology in order to specialized research the support pattern between networks. In terms of initial failure from power grid or communication network, we find the remaining survival fraction is greatly disparate, and the failure initially from power grid is more harmful than failure initially from communication network, which all show the vulnerability of interdependency and meantime guide us to pay more attention to the protection measures for power grid.

Information and communication technologies are extensively used to monitor and control electric microgrids. Although, such innovation enhance self healing, resilience, and efficiency of the energy infrastructure, it brings emerging security threats to be a critical challenge. In the context of microgrid, the cyber vulnerabilities may be exploited by malicious users for manipulate system parameters, meter measurements and price information. In particular, malware may be used to acquire direct access to monitor and control devices in order to destabilize the microgrid ecosystem. In this paper, we exploit a sandbox to analyze security vulnerability to malware of involved embedded smart-devices, by monitoring at different abstraction levels potential malicious behaviors. In this direction, the CoSSMic project represents a relevant case study.

Cyber security is a crucial factor for modern power system as many applications are heavily relied on the result of state estimation. Therefore, it is necessary to assess and enhance cyber security for new applications in power system. As an emerging technology, smart grid topology control has been investigated in stability and reliability perspectives while the associated cyber security issue is not studied before. In successful false data injection attack (FDIA) against AC state estimation, attacker could alter online stability check result by decreasing real power flow measurement on the switching target line to undermine physical system stability in topology control. The physical impact of FDIA on system control operation and stability are illustrated. The vulnerability is discussed on perfect FDIA and imperfect FDIA against residue based bad data detection and corresponding countermeasure is proposed to secure critical substations in the system. The vulnerability and countermeasure are demonstrated on IEEE 24 bus reliability test system (RTS).

With the increasing scale of the network, the power information system has many characteristics, such as large number of nodes, complicated structure, diverse network protocols and abundant data, which make the network intrusion detection system difficult to detect real alarms. The current security technologies cannot meet the actual power system network security operation and protection requirements. Based on the attacker ability, the vulnerability information and the existing security protection configuration, we construct the attack sub-graphs by using the parallel distributed computing method and combine them into the whole network attack graph. The vulnerability exploit degree, attacker knowledge, attack proficiency, attacker willingness and the confidence level of the attack evidence are used to construct the security evaluation index system of the power information network system to calculate the attack probability value of each node of the attack graph. According to the probability of occurrence of each node attack, the pre-order attack path will be formed and then the most likely attack path and attack targets will be got to achieve the identification of attack intent.

Although connecting a microgrid to modern power systems can alleviate issues arising from a large penetration of distributed generation, it can also cause severe voltage instability problems. This paper presents an online method to analyze voltage security in a microgrid using convolutional neural networks. To transform the traditional voltage stability problem into a classification problem, three steps are considered: 1) creating data sets using offline simulation results; 2) training the model with dimensional reduction and convolutional neural networks; 3) testing the online data set and evaluating performance. A case study in the modified IEEE 14-bus system shows the accuracy of the proposed analysis method increases by 6% compared to back-propagation neural network and has better performance than decision tree and support vector machine. The proposed algorithm has great potential in future applications.