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Author: Bo Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Moving target defense (MTD) in the power system is a promising defense strategy to detect false data injection (FDI) attacks against state estimation by using distributed flexible AC transmission system (D-FACTS) devices. Optimal planning and operation are two essential stages in the MTD application. MTD planning determines the optimal allocation of D-FACTS devices, while MTD operation decides the optimal D-FACTS setpoints under different load conditions in real-time. However, most MTD works focus on studying the MTD operation methods and neglect MTD planning. It is generally assumed that all lines are equipped with D-FACTS devices, which is the most expensive MTD planning solution. This dissertation separates MTD planning and MTD operation as two independent problems by distinguishing their roles in attack detection effectiveness, MTD application costs, and MTD hiddenness. The contributions of this work are three-fold as follows. Firstly, this dissertation proves that MTD planning can determine the MTD detection effectiveness, regardless of D-FACTS device setpoints in MTD operation. This work designs max-rank MTD planning algorithms by using the minimum number of D-FACTS devices to ensure MTD detection effectiveness and minimize the MTD planning cost. It is proved that any MTDs under proposed planning algorithms have the maximum rank of its composite matrix, a widely used metric of the MTD detection effectiveness. In addition, this work further points out the maximum rank of the composite matrix is not strictly equivalent to maximal MTD detection effectiveness. Three types of unprotected buses in MTD are identified, and attack detecting probability (ADP) is introduced as a novel metric for measuring the detection effectiveness of MTD planning. It is proved that the rank of the composite matrix merely represents the lower bound of ADP, while the number of unprotected buses determines the upper bound of ADP. Then, a novel graph-theory-based planning algorithm is proposed to achieve maximal MTD detection effectiveness. Secondly, this dissertation highlights that MTD operation ought to focus on reducing the MTD operation cost. This work proposes an AC optimal power flow (ACOPF) model considering D-FACTS devices as an MTD operation model, in which the reactance of D-FACTS equipped lines are introduced as decision variables to minimize system losses and generation costs. The proposed model can be used by system operators to achieve economic and cybersecure system operations. In addition, this dissertation rigorously derives the gradient and Hessian matrices of the objective function and constraints with respect to line reactance, which are further used to build an interior-point solver of the proposed ACOPF model. Finally, this dissertation designs the optimal planning and operation of D-FACTS devices for hidden MTD (HMTD), which is a superior MTD method stealthy to sophisticated attackers. A depth-first-search-based MTD planning algorithm is proposed to guarantee the MTD hiddenness while maximizing the rank of its composite matrix and covering all necessary buses. Additionally, this work proposes DC- and AC-HMTD operation models to determine the setpoints of D-FACTS devices. The optimization-based DC-HMTD model outperforms the existing HMTD operation in terms of CPU time and detection effectiveness. The ACOPF-based HMTD operation model ensures the hiddenness and minimizes the generation cost to utilize the economic benefits of D-FACTS devices. Comparative numerical results on multiple systems show the efficacy of the proposed planning and operation approaches in achieving high detecting effectiveness and MTD hiddenness.
Author: Bo Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Moving target defense (MTD) in the power system is a promising defense strategy to detect false data injection (FDI) attacks against state estimation by using distributed flexible AC transmission system (D-FACTS) devices. Optimal planning and operation are two essential stages in the MTD application. MTD planning determines the optimal allocation of D-FACTS devices, while MTD operation decides the optimal D-FACTS setpoints under different load conditions in real-time. However, most MTD works focus on studying the MTD operation methods and neglect MTD planning. It is generally assumed that all lines are equipped with D-FACTS devices, which is the most expensive MTD planning solution. This dissertation separates MTD planning and MTD operation as two independent problems by distinguishing their roles in attack detection effectiveness, MTD application costs, and MTD hiddenness. The contributions of this work are three-fold as follows. Firstly, this dissertation proves that MTD planning can determine the MTD detection effectiveness, regardless of D-FACTS device setpoints in MTD operation. This work designs max-rank MTD planning algorithms by using the minimum number of D-FACTS devices to ensure MTD detection effectiveness and minimize the MTD planning cost. It is proved that any MTDs under proposed planning algorithms have the maximum rank of its composite matrix, a widely used metric of the MTD detection effectiveness. In addition, this work further points out the maximum rank of the composite matrix is not strictly equivalent to maximal MTD detection effectiveness. Three types of unprotected buses in MTD are identified, and attack detecting probability (ADP) is introduced as a novel metric for measuring the detection effectiveness of MTD planning. It is proved that the rank of the composite matrix merely represents the lower bound of ADP, while the number of unprotected buses determines the upper bound of ADP. Then, a novel graph-theory-based planning algorithm is proposed to achieve maximal MTD detection effectiveness. Secondly, this dissertation highlights that MTD operation ought to focus on reducing the MTD operation cost. This work proposes an AC optimal power flow (ACOPF) model considering D-FACTS devices as an MTD operation model, in which the reactance of D-FACTS equipped lines are introduced as decision variables to minimize system losses and generation costs. The proposed model can be used by system operators to achieve economic and cybersecure system operations. In addition, this dissertation rigorously derives the gradient and Hessian matrices of the objective function and constraints with respect to line reactance, which are further used to build an interior-point solver of the proposed ACOPF model. Finally, this dissertation designs the optimal planning and operation of D-FACTS devices for hidden MTD (HMTD), which is a superior MTD method stealthy to sophisticated attackers. A depth-first-search-based MTD planning algorithm is proposed to guarantee the MTD hiddenness while maximizing the rank of its composite matrix and covering all necessary buses. Additionally, this work proposes DC- and AC-HMTD operation models to determine the setpoints of D-FACTS devices. The optimization-based DC-HMTD model outperforms the existing HMTD operation in terms of CPU time and detection effectiveness. The ACOPF-based HMTD operation model ensures the hiddenness and minimizes the generation cost to utilize the economic benefits of D-FACTS devices. Comparative numerical results on multiple systems show the efficacy of the proposed planning and operation approaches in achieving high detecting effectiveness and MTD hiddenness.
Author: Hakim Hacid
Publisher: Springer Nature
ISBN: 3030914313
Category : Computers
Languages : en
Pages : 919
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Book Description
This book constitutes the proceedings of the 19th International Conference on Service-Oriented Computing, ICSOC 2020, which is held virtually in November 2021. The 29 full, 28 short, and 3 vision papers included in this volume were carefully reviewed and selected from 189 submissions. They were organized in topical sections named: Blockchains and smart contracts, Architectures, microservices and APIs, Applications, Internet-of-Things, crowdsourced, social, and conversational services, Service composition and recommendation, Cloud computing, and Edge computing.
Author: Beibei Li
Publisher: Springer Nature
ISBN: 3030586723
Category : Technology & Engineering
Languages : en
Pages : 169
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Book Description
This book discusses cybersecurity issues of smart grid cyber-physical systems, focusing on the detection techniques against false data injection attacks. The authors discuss passive and proactive techniques that combat and mitigate two categories of false data injection attacks, false measurement data injections and false command data injections in smart grid cyber-physical systems. These techniques are easy to follow for either professionals or beginners. With this book, readers can quickly get an overview of this topic and get ideas of new solutions for false data injections in smart grid cyber-physical systems. Readers include researchers, academics, students, and professionals. Presents a comprehensive summary for the detection techniques of false data injection attacks in smart grid cyber-physical systems; Reviews false data injections for either measurement data or command data; Analyzes passive and proactive approaches to smart grid cyber-physical systems.
Author: Sushil Jajodia
Publisher: Springer Science & Business Media
ISBN: 1461454166
Category : Computers
Languages : en
Pages : 210
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Book Description
Our cyber defenses are static and are governed by lengthy processes, e.g., for testing and security patch deployment. Adversaries could plan their attacks carefully over time and launch attacks at cyber speeds at any given moment. We need a new class of defensive strategies that would force adversaries to continually engage in reconnaissance and re-planning of their cyber operations. One such strategy is to present adversaries with a moving target where the attack surface of a system keeps changing. Moving Target Defense II: Application of Game Theory and Adversarial Modeling includes contributions from world experts in the cyber security field. In the first volume of MTD, we presented MTD approaches based on software transformations, and MTD approaches based on network and software stack configurations. In this second volume of MTD, a group of leading researchers describe game theoretic, cyber maneuver, and software transformation approaches for constructing and analyzing MTD systems. Designed as a professional book for practitioners and researchers working in the cyber security field, advanced -level students and researchers focused on computer science will also find this book valuable as a secondary text book or reference.
Author: Alberto Miguez Dominguez
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
A smart grid is an electricity grid that allows two-way flow of electricity in its network, enabling consumers to have better control over their electricity usage while reducing the operations and management costs for utilities. The communication devices in smart grids have increased the integration of renewable energy systems, such as wind and solar, and have proven to be very effective at helping restore power faster when a power disturbance occurs. In recent years, the integration of more communication devices in the power grid has opened the opportunity for more Data Integrity cyber-physical attacks. Smart grids can be a prime target for these types of attacks which can lead to cascading failures in a transmission system. False Data Injection attacks, a type of Data Integrity cyber-physical attack, can manipulate the system's measurements, and therefore, the power dispatch, in a way that can make the lines in the system overflow. This type of attack could theoretically be performed without the operator ever knowing that there was an attack, and it can cause power outages and even system blackouts. The purpose of this thesis is to implement a False Data Injection attack strategy on targeted buses that bypass DC state estimation and develop a new algorithm that can detect them using AC state estimation with Kalman Filters. Possible attacks on the system will be considered and Kalman Filters will be used to aid in the detection of bad data injections in the system that would allow the operator to know if there is an attack currently happening. The proposed novel algorithm was developed in MATLAB and tested using a modified IEEE 14 bus-system with a fixed power flow between lines of 25 MW.
Author: Christian Salem
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS.
Author: Yun Li
Publisher:
ISBN: 9780355151299
Category :
Languages : en
Pages :
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Book Description
Moving target defense system changes the attack surface at the run time to eliminates the attacker's asymmetric advantage of time. Common moving target defense techniques include thwarting ongoing attacks and increasing the time and difficulty required to discover the target system's configuration. Our moving target defense systems deploy cost-sensitive models and determine detection strategy, response option, or system configuration with pursuing minimum cost solutions. By adjusting parameters settings, the moving target defense systems adapt to user preference and surrounding environments, dynamically. In this dissertation, we will present moving target defense frameworks that accomplish different tasks including response, detection, and consensus. For each framework, we will discuss the model and application example in detail.
Author: Prayush Khadka
Publisher:
ISBN:
Category : Difference sets
Languages : en
Pages : 0
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Book Description
As the modern electric grid communicates with each other, the system's inherent security vulnerabilities start becoming a problem for efficient operation. Various attacks on the system for nefarious means diminish the system's capabilities. False Data Injection Attack (FDIA) manipulates the meters' reading and shows inaccurate system values. FDIA negatively affects the electricity grid's usage, cost, and future planning for all stakeholders. This study seeks to find a way to efficiently detect the presence of false data attacks in the power system and presents a method of early detection of false data injection attacks. Two different processes are employed; Kullback-Leibler Divergence and Maximum Difference. Kullback-Leibler Divergence calculates the distance between two probability distributions, while Maximum Difference measures the largest possible difference between two values. The analysis is done on the load data obtained from New York Independent System Operator (NYISO). The results indicate that the suggested investigation presents methods and mechanisms where false data injection attacks of different quantities are detected with high accuracy while having false positives under a reasonable value. The result points out that Maximum Difference is preferable to Kullback-Liebler Divergence for False Data Detection Attack detection. The analysis outcomes also support the described process in early detection of the attack on the power system. Further study can be done to implement the methods in real-time attack detection scenarios.
Author: Sushil Jajodia
Publisher: Springer Science & Business Media
ISBN: 1461409772
Category : Computers
Languages : en
Pages : 196
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Book Description
Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats was developed by a group of leading researchers. It describes the fundamental challenges facing the research community and identifies new promising solution paths. Moving Target Defense which is motivated by the asymmetric costs borne by cyber defenders takes an advantage afforded to attackers and reverses it to advantage defenders. Moving Target Defense is enabled by technical trends in recent years, including virtualization and workload migration on commodity systems, widespread and redundant network connectivity, instruction set and address space layout randomization, just-in-time compilers, among other techniques. However, many challenging research problems remain to be solved, such as the security of virtualization infrastructures, secure and resilient techniques to move systems within a virtualized environment, automatic diversification techniques, automated ways to dynamically change and manage the configurations of systems and networks, quantification of security improvement, potential degradation and more. Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats is designed for advanced -level students and researchers focused on computer science, and as a secondary text book or reference. Professionals working in this field will also find this book valuable.
Author: Rui Zhuang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The static nature of cyber systems gives attackers a valuable and asymmetric advantage - time. To eliminate this asymmetric advantage, a new approach, called Moving Target Defense (MTD) has emerged as a potential solution. MTD system seeks to proactively change system configurations to invalidate the knowledge learned by the attacker and force them to spend more effort locating and re-locating vulnerabilities. While it sounds promising, the approach is so new that there is no standard definition of what an MTD is, what is meant by diversification and randomization, or what metrics to define the effectiveness of such systems. Moreover, the changing nature of MTD violates two basic assumptions about the conventional attack surface notion. One is that the attack surface remains unchanged during an attack and the second is that it is always reachable. Therefore, a new attack surface definition is needed. To address these issues, I propose that a theoretical framework for MTD be defined. The framework should clarify the most basic questions such as what an MTD system is and its properties such as adaptation, diversification and randomization. The framework should reveal what is meant by gaining and losing knowledge, and what are different attack types. To reason over the interactions between attacker and MTD system, the framework should define key concepts such as attack surface, adaptation surface and engagement surface. Based on that, this framework should allow MTD system designers to decide how to use existing configuration choices and functionality diversification to increase security. It should allow them to analyze the effectiveness of adapting various combinations of different configuration aspects to thwart different types of attacks. To support analysis, the frame- work should include an analytical model that can be used by designers to determine how different parameter settings will impact system security.
