ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Electrical Engineering
Description
As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are one of the main factors driving the behavior of a power system and they depend on external phenomena which are not captured by traditional simulation tools. Thus, accurate models that capture the fundamental characteristics of time-series load dataare necessary.
In the first part of this dissertation, an example of successful application of machine learning algorithms that leverage load data is presented. Prior work has shown that power systems energy management systems are vulnerable to false data injection attacks against state estimation. Here, a data-driven approach for the detection and localization of such attacks is proposed. The detector uses historical data to learn the normal behavior of the loads in a system and subsequently identify if any of the real-time observed measurements are being manipulated by an attacker.
The second part of this work focuses on the design of generative models for time-series load data. Two separate techniques are used to learn load behaviors from real datasets and exploiting them to generate realistic synthetic data. The first approach is based on principal component analysis (PCA), which is used to extract common temporal patterns from real data. The second method leverages conditional generative adversarial networks (cGANs) and it overcomes the limitations of the PCA-based model while providing greater and more nuanced control on the generation of specific
types of load profiles. Finally, these two classes of models are combined in a multi-resolution generative scheme which is capable of producing any amount of time-series load data at any sampling resolution, for lengths ranging from a few seconds to years.
ContributorsPinceti, Andrea (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Pal, Anamitra (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the era of big data, more and more decisions and recommendations are being made by machine learning (ML) systems and algorithms. Despite their many successes, there have been notable deficiencies in the robustness, rigor, and reliability of these ML systems, which have had detrimental societal impacts. In the next generation of ML, these significant challenges must be addressed through careful algorithmic design, and it is crucial that practitioners and meta-algorithms have the necessary tools to construct ML models that align with human values and interests. In an effort to help address these problems, this dissertation studies a tunable loss function called α-loss for the ML setting of classification. The alpha-loss is a hyperparameterized loss function originating from information theory that continuously interpolates between the exponential (alpha = 1/2), log (alpha = 1), and 0-1 (alpha = infinity) losses, hence providing a holistic perspective of several classical loss functions in ML. Furthermore, the alpha-loss exhibits unique operating characteristics depending on the value (and different regimes) of alpha; notably, for alpha > 1, alpha-loss robustly trains models when noisy training data is present. Thus, the alpha-loss can provide robustness to ML systems for classification tasks, and this has bearing in many applications, e.g., social media, finance, academia, and medicine; indeed, results are presented where alpha-loss produces more robust logistic regression models for COVID-19 survey data with gains over state of the art algorithmic approaches.
ContributorsSypherd, Tyler (Author) / Sankar, Lalitha (Thesis advisor) / Berisha, Visar (Committee member) / Dasarathy, Gautam (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2022
Description
Edge networks pose unique challenges for machine learning and network management. The primary objective of this dissertation is to study deep learning and adaptive control aspects of edge networks and to address some of the unique challenges therein. This dissertation explores four particular problems of interest at the intersection of edge intelligence, deep learning and network management. The first problem explores the learning of generative models in edge learning setting. Since the learning tasks in similar environments share model similarity, it is plausible to leverage pre-trained generative models from other edge nodes. Appealing to optimal transport theory tailored towards Wasserstein-1 generative adversarial networks, this part aims to develop a framework which systematically optimizes the generative model learning performance using local data at the edge node while exploiting the adaptive coalescence of pre-trained generative models from other nodes. In the second part, a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data, is considered. The unreliable nature of wireless connectivity, togetherwith the constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Therefore, a Stochastic Gradient Descent based bandlimited coordinate descent algorithm is designed for such settings. The third part explores the adaptive traffic engineering algorithms in a dynamic network environment. The ages of traffic measurements exhibit significant variation due to asynchronization and random communication delays between routers and controllers. Inspired by the software defined networking architecture, a controller-assisted distributed routing scheme with recursive link weight reconfigurations, accounting for the impact of measurement ages and routing instability, is devised. The final part focuses on developing a federated learning based framework for traffic reshaping of electric vehicle (EV) charging. The absence of private EV owner information and scattered EV charging data among charging stations motivates the utilization of a federated learning approach. Federated learning algorithms are devised to minimize peak EV charging demand both spatially and temporarily, while maximizing the charging station profit.
ContributorsDedeoglu, Mehmet (Author) / Zhang, Junshan (Thesis advisor) / Kosut, Oliver (Committee member) / Zhang, Yanchao (Committee member) / Fan, Deliang (Committee member) / Arizona State University (Publisher)
Created2021
Description
The presence of strategic agents can pose unique challenges to data collection and distributed learning. This dissertation first explores the social network dimension of data collection markets, and then focuses on how the strategic agents can be efficiently and effectively incentivized to cooperate in distributed machine learning frameworks.
The first problem explores the impact of social learning in collecting and trading unverifiable information where a data collector purchases data from users through a payment mechanism. Each user starts with a personal signal which represents the knowledge about the underlying state the data collector desires to learn. Through social interactions, each user also acquires additional information from his neighbors in the social network. It is revealed that both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation for a given total payment budget. In the second half, a federated learning scheme to train a global learning model with strategic agents, who are not bound to contribute their resources unconditionally, is considered. Since the agents are not obliged to provide their true stochastic gradient updates and the server is not capable of directly validating the authenticity of reported updates, the learning process may reach a noncooperative equilibrium. First, the actions of the agents are assumed to be binary: cooperative or defective. If the cooperative action is taken, the agent sends a privacy-preserved version of stochastic gradient signal. If the defective action is taken, the agent sends an arbitrary uninformative noise signal. Furthermore, this setup is extended into the scenarios with more general actions spaces where the quality of the stochastic gradient updates have a range of discrete levels. The proposed methodology evaluates each agent's stochastic gradient according to a reference gradient estimate which is constructed from the gradients provided by other agents, and rewards the agent based on that evaluation.
ContributorsAkbay, Abdullah Basar (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Kosut, Oliver (Committee member) / Ewaisha, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
Our daily life is becoming more and more reliant on services provided by the infrastructures
power, gas , communication networks. Ensuring the security of these
infrastructures is of utmost importance. This task becomes ever more challenging as
the inter-dependence among these infrastructures grows and a security breach in one
infrastructure can spill over to the others. The implication is that the security practices/
analysis recommended for these infrastructures should be done in coordination.
This thesis, focusing on the power grid, explores strategies to secure the system that
look into the coupling of the power grid to the cyber infrastructure, used to manage
and control it, and to the gas grid, that supplies an increasing amount of reserves to
overcome contingencies.
The first part (Part I) of the thesis, including chapters 2 through 4, focuses on
the coupling of the power and the cyber infrastructure that is used for its control and
operations. The goal is to detect malicious attacks gaining information about the
operation of the power grid to later attack the system. In chapter 2, we propose a
hierarchical architecture that correlates the analysis of high resolution Micro-Phasor
Measurement Unit (microPMU) data and traffic analysis on the Supervisory Control
and Data Acquisition (SCADA) packets, to infer the security status of the grid and
detect the presence of possible intruders. An essential part of this architecture is
tied to the analysis on the microPMU data. In chapter 3 we establish a set of anomaly
detection rules on microPMU data that
flag "abnormal behavior". A placement strategy
of microPMU sensors is also proposed to maximize the sensitivity in detecting anomalies.
In chapter 4, we focus on developing rules that can localize the source of an events
using microPMU to further check whether a cyber attack is causing the anomaly, by
correlating SCADA traffic with the microPMU data analysis results. The thread that
unies the data analysis in this chapter is the fact that decision are made without fully estimating the state of the system; on the contrary, decisions are made using
a set of physical measurements that falls short by orders of magnitude to meet the
needs for observability. More specifically, in the first part of this chapter (sections 4.1-
4.2), using microPMU data in the substation, methodologies for online identification of
the source Thevenin parameters are presented. This methodology is used to identify
reconnaissance activity on the normally-open switches in the substation, initiated
by attackers to gauge its controllability over the cyber network. The applications
of this methodology in monitoring the voltage stability of the grid is also discussed.
In the second part of this chapter (sections 4.3-4.5), we investigate the localization
of faults. Since the number of PMU sensors available to carry out the inference
is insufficient to ensure observability, the problem can be viewed as that of under-sampling
a "graph signal"; the analysis leads to a PMU placement strategy that can
achieve the highest resolution in localizing the fault, for a given number of sensors.
In both cases, the results of the analysis are leveraged in the detection of cyber-physical
attacks, where microPMU data and relevant SCADA network traffic information
are compared to determine if a network breach has affected the integrity of the system
information and/or operations.
In second part of this thesis (Part II), the security analysis considers the adequacy
and reliability of schedules for the gas and power network. The motivation for
scheduling jointly supply in gas and power networks is motivated by the increasing
reliance of power grids on natural gas generators (and, indirectly, on gas pipelines)
as providing critical reserves. Chapter 5 focuses on unveiling the challenges and
providing solution to this problem.
power, gas , communication networks. Ensuring the security of these
infrastructures is of utmost importance. This task becomes ever more challenging as
the inter-dependence among these infrastructures grows and a security breach in one
infrastructure can spill over to the others. The implication is that the security practices/
analysis recommended for these infrastructures should be done in coordination.
This thesis, focusing on the power grid, explores strategies to secure the system that
look into the coupling of the power grid to the cyber infrastructure, used to manage
and control it, and to the gas grid, that supplies an increasing amount of reserves to
overcome contingencies.
The first part (Part I) of the thesis, including chapters 2 through 4, focuses on
the coupling of the power and the cyber infrastructure that is used for its control and
operations. The goal is to detect malicious attacks gaining information about the
operation of the power grid to later attack the system. In chapter 2, we propose a
hierarchical architecture that correlates the analysis of high resolution Micro-Phasor
Measurement Unit (microPMU) data and traffic analysis on the Supervisory Control
and Data Acquisition (SCADA) packets, to infer the security status of the grid and
detect the presence of possible intruders. An essential part of this architecture is
tied to the analysis on the microPMU data. In chapter 3 we establish a set of anomaly
detection rules on microPMU data that
flag "abnormal behavior". A placement strategy
of microPMU sensors is also proposed to maximize the sensitivity in detecting anomalies.
In chapter 4, we focus on developing rules that can localize the source of an events
using microPMU to further check whether a cyber attack is causing the anomaly, by
correlating SCADA traffic with the microPMU data analysis results. The thread that
unies the data analysis in this chapter is the fact that decision are made without fully estimating the state of the system; on the contrary, decisions are made using
a set of physical measurements that falls short by orders of magnitude to meet the
needs for observability. More specifically, in the first part of this chapter (sections 4.1-
4.2), using microPMU data in the substation, methodologies for online identification of
the source Thevenin parameters are presented. This methodology is used to identify
reconnaissance activity on the normally-open switches in the substation, initiated
by attackers to gauge its controllability over the cyber network. The applications
of this methodology in monitoring the voltage stability of the grid is also discussed.
In the second part of this chapter (sections 4.3-4.5), we investigate the localization
of faults. Since the number of PMU sensors available to carry out the inference
is insufficient to ensure observability, the problem can be viewed as that of under-sampling
a "graph signal"; the analysis leads to a PMU placement strategy that can
achieve the highest resolution in localizing the fault, for a given number of sensors.
In both cases, the results of the analysis are leveraged in the detection of cyber-physical
attacks, where microPMU data and relevant SCADA network traffic information
are compared to determine if a network breach has affected the integrity of the system
information and/or operations.
In second part of this thesis (Part II), the security analysis considers the adequacy
and reliability of schedules for the gas and power network. The motivation for
scheduling jointly supply in gas and power networks is motivated by the increasing
reliance of power grids on natural gas generators (and, indirectly, on gas pipelines)
as providing critical reserves. Chapter 5 focuses on unveiling the challenges and
providing solution to this problem.
ContributorsJamei, Mahdi (Author) / Scaglioe, Anna (Thesis advisor) / Ayyanar, Raja (Committee member) / Hedman, Kory W (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2018
Description
Both two-way relays (TWR) and full-duplex (FD) radios are spectrally efficient, and their integration shows great potential to further improve the spectral efficiency, which offers a solution to the fifth generation wireless systems. High quality channel state information (CSI) are the key components for the implementation and the performance of the FD TWR system, making channel estimation in FD TWRs crucial.
The impact of channel estimation on spectral efficiency in half-duplex multiple-input-multiple-output (MIMO) TWR systems is investigated. The trade-off between training and data energy is proposed. In the case that two sources are symmetric in power and number of antennas, a closed-form for the optimal ratio of data energy to total energy is derived. It can be shown that the achievable rate is a monotonically increasing function of the data length. The asymmetric case is discussed as well.
Efficient and accurate training schemes for FD TWRs are essential for profiting from the inherent spectrally efficient structures of both FD and TWRs. A novel one-block training scheme with a maximum likelihood (ML) estimator is proposed to estimate the channels between the nodes and the residual self-interference (RSI) channel simultaneously. Baseline training schemes are also considered to compare with the one-block scheme. The Cramer-Rao bounds (CRBs) of the training schemes are derived and analyzed by using the asymptotic properties of Toeplitz matrices. The benefit of estimating the RSI channel is shown analytically in terms of Fisher information.
To obtain fundamental and analytic results of how the RSI affects the spectral efficiency, one-way FD relay systems are studied. Optimal training design and ML channel estimation are proposed to estimate the RSI channel. The CRBs are derived and analyzed in closed-form so that the optimal training sequence can be found via minimizing the CRB. Extensions of the training scheme to frequency-selective channels and multiple relays are also presented.
Simultaneously sensing and transmission in an FD cognitive radio system with MIMO is considered. The trade-off between the transmission rate and the detection accuracy is characterized by the sum-rate of the primary and the secondary users. Different beamforming and combining schemes are proposed and compared.
The impact of channel estimation on spectral efficiency in half-duplex multiple-input-multiple-output (MIMO) TWR systems is investigated. The trade-off between training and data energy is proposed. In the case that two sources are symmetric in power and number of antennas, a closed-form for the optimal ratio of data energy to total energy is derived. It can be shown that the achievable rate is a monotonically increasing function of the data length. The asymmetric case is discussed as well.
Efficient and accurate training schemes for FD TWRs are essential for profiting from the inherent spectrally efficient structures of both FD and TWRs. A novel one-block training scheme with a maximum likelihood (ML) estimator is proposed to estimate the channels between the nodes and the residual self-interference (RSI) channel simultaneously. Baseline training schemes are also considered to compare with the one-block scheme. The Cramer-Rao bounds (CRBs) of the training schemes are derived and analyzed by using the asymptotic properties of Toeplitz matrices. The benefit of estimating the RSI channel is shown analytically in terms of Fisher information.
To obtain fundamental and analytic results of how the RSI affects the spectral efficiency, one-way FD relay systems are studied. Optimal training design and ML channel estimation are proposed to estimate the RSI channel. The CRBs are derived and analyzed in closed-form so that the optimal training sequence can be found via minimizing the CRB. Extensions of the training scheme to frequency-selective channels and multiple relays are also presented.
Simultaneously sensing and transmission in an FD cognitive radio system with MIMO is considered. The trade-off between the transmission rate and the detection accuracy is characterized by the sum-rate of the primary and the secondary users. Different beamforming and combining schemes are proposed and compared.
ContributorsLi, Xiaofeng (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel W (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2018
Description
In the past few decades, there has been a remarkable shift in the boundary between public and private information. The application of information technology and electronic communications allow service providers (businesses) to collect a large amount of data. However, this ``data collection" process can put the privacy of users at risk and also lead to user reluctance in accepting services or sharing data. This dissertation first investigates privacy sensitive consumer-retailers/service providers interactions under different scenarios, and then focuses on a unified framework for various information-theoretic privacy and privacy mechanisms that can be learned directly from data.
Existing approaches such as differential privacy or information-theoretic privacy try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. The first part of this dissertation introduces models to study consumer-retailer interaction problems and to better understand how retailers/service providers can balance their revenue objectives while being sensitive to user privacy concerns. This dissertation considers the following three scenarios: (i) the consumer-retailer interaction via personalized advertisements; (ii) incentive mechanisms that electrical utility providers need to offer for privacy sensitive consumers with alternative energy sources; (iii) the market viability of offering privacy guaranteed free online services. We use game-theoretic models to capture the behaviors of both consumers and retailers, and provide insights for retailers to maximize their profits when interacting with privacy sensitive consumers.
Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. In the second part, a novel context-aware privacy framework called generative adversarial privacy (GAP) is introduced. Inspired by recent advancements in generative adversarial networks, GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. For appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. Both synthetic and real-world datasets are used to show that GAP can greatly reduce the adversary's capability of inferring private information at a small cost of distorting the data.
Existing approaches such as differential privacy or information-theoretic privacy try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. The first part of this dissertation introduces models to study consumer-retailer interaction problems and to better understand how retailers/service providers can balance their revenue objectives while being sensitive to user privacy concerns. This dissertation considers the following three scenarios: (i) the consumer-retailer interaction via personalized advertisements; (ii) incentive mechanisms that electrical utility providers need to offer for privacy sensitive consumers with alternative energy sources; (iii) the market viability of offering privacy guaranteed free online services. We use game-theoretic models to capture the behaviors of both consumers and retailers, and provide insights for retailers to maximize their profits when interacting with privacy sensitive consumers.
Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. In the second part, a novel context-aware privacy framework called generative adversarial privacy (GAP) is introduced. Inspired by recent advancements in generative adversarial networks, GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. For appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. Both synthetic and real-world datasets are used to show that GAP can greatly reduce the adversary's capability of inferring private information at a small cost of distorting the data.
ContributorsHuang, Chong (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Nedich, Angelia (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2018
Description
Multiple-input multiple-output systems have gained focus in the last decade due to the benefits they provide in enhancing the quality of communications. On the other hand, full-duplex communication has attracted remarkable attention due to its ability to improve the spectral efficiency compared to the existing half-duplex systems. Using full-duplex communications on MIMO co-operative networks can provide us solutions that can completely outperform existing systems with simultaneous transmission and reception at high data rates.
This thesis considers a full-duplex MIMO relay which amplifies and forwards the received signals, between a source and a destination that do not a have line of sight. Full-duplex mode raises the problem of self-interference. Though all the links in the system undergo frequency flat fading, the end-to-end effective channel is frequency selective. This is due to the imperfect cancellation of the self-interference at the relay and this residual self-interference acts as intersymbol interference at the destination which is treated by equalization. This also leads to complications in form of recursive equations to determine the input-output relationship of the system. This also leads to complications in the form of recursive equations to determine the input-output relationship of the system.
To overcome this, a signal flow graph approach using Mason's gain formula is proposed, where the effective channel is analyzed with keen notice to every loop and path the signal traverses. This gives a clear understanding and awareness about the orders of the polynomials involved in the transfer function, from which desired conclusions can be drawn. But the complexity of Mason's gain formula increases with the number of antennas at relay which can be overcome by the proposed linear algebraic method. Input-output relationship derived using simple concepts of linear algebra can be generalized to any number of antennas and the computation complexity is comparatively very low.
For a full-duplex amplify-and-forward MIMO relay system, assuming equalization at the destination, new mechanisms have been implemented at the relay that can compensate the effect of residual self-interference namely equal-gain transmission and antenna selection. Though equal-gain transmission does not perform better than the maximal ratio transmission, a trade-off can be made between performance and implementation complexity. Using the proposed antenna selection strategy, one pair of transmit-receive antennas at the relay is selected based on four selection criteria discussed. Outage probability analysis is performed for all the strategies presented and detailed comparison has been established. Considering minimum mean-squared error decision feedback equalizer at the destination, a bound on the outage probability has been obtained for the antenna selection case and is used for comparisons. A cross-over point is observed while comparing the outage probabilities of equal-gain transmission and antenna selection techniques, as the signal-to-noise ratio increases and from that point antenna selection outperforms equal-gain transmission and this is explained by the fact of reduced residual self-interference in antenna selection method.
This thesis considers a full-duplex MIMO relay which amplifies and forwards the received signals, between a source and a destination that do not a have line of sight. Full-duplex mode raises the problem of self-interference. Though all the links in the system undergo frequency flat fading, the end-to-end effective channel is frequency selective. This is due to the imperfect cancellation of the self-interference at the relay and this residual self-interference acts as intersymbol interference at the destination which is treated by equalization. This also leads to complications in form of recursive equations to determine the input-output relationship of the system. This also leads to complications in the form of recursive equations to determine the input-output relationship of the system.
To overcome this, a signal flow graph approach using Mason's gain formula is proposed, where the effective channel is analyzed with keen notice to every loop and path the signal traverses. This gives a clear understanding and awareness about the orders of the polynomials involved in the transfer function, from which desired conclusions can be drawn. But the complexity of Mason's gain formula increases with the number of antennas at relay which can be overcome by the proposed linear algebraic method. Input-output relationship derived using simple concepts of linear algebra can be generalized to any number of antennas and the computation complexity is comparatively very low.
For a full-duplex amplify-and-forward MIMO relay system, assuming equalization at the destination, new mechanisms have been implemented at the relay that can compensate the effect of residual self-interference namely equal-gain transmission and antenna selection. Though equal-gain transmission does not perform better than the maximal ratio transmission, a trade-off can be made between performance and implementation complexity. Using the proposed antenna selection strategy, one pair of transmit-receive antennas at the relay is selected based on four selection criteria discussed. Outage probability analysis is performed for all the strategies presented and detailed comparison has been established. Considering minimum mean-squared error decision feedback equalizer at the destination, a bound on the outage probability has been obtained for the antenna selection case and is used for comparisons. A cross-over point is observed while comparing the outage probabilities of equal-gain transmission and antenna selection techniques, as the signal-to-noise ratio increases and from that point antenna selection outperforms equal-gain transmission and this is explained by the fact of reduced residual self-interference in antenna selection method.
ContributorsJonnalagadda, Geeta Sankar Kalyan (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Bliss, Daniel (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2018
Description
The large distributed electric power system is a hierarchical network involving the
transportation of power from the sources of power generation via an intermediate
densely connected transmission network to a large distribution network of end-users
at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-
mission/ distribution), the system is managed and monitored with a combination of
(a) supervisory control and data acquisition (SCADA); and (b) energy management
systems (EMSs) that process the collected data and make control and actuation de-
cisions using the collected data. However, at all levels of the hierarchy, both SCADA
and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the
electric power infrastructure, cyber attacks can have severe economic and social con-
sequences.
This thesis focuses on cyber attacks on SCADA and EMS at the transmission
level of the electric power system. The goal is to study the consequences of three
classes of cyber attacks that can change topology data. These classes include: (i)
unobservable state-preserving cyber attacks that only change the topology data; (ii)
unobservable state-and-topology cyber-physical attacks that change both states and
topology data to enable a coordinated physical and cyber attack; and (iii) topology-
targeted man-in-the-middle (MitM) communication attacks that alter topology data
shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on
the unobservable attacks on single regional EMS while class (iii) focuses on the MitM
attacks on communication links between regional EMSs. For each class of attacks,
the theoretical attack model and the implementation of attacks are provided, and the
worst-case attack and its consequences are exhaustively studied. In particularly, for
class (ii), a two-stage optimization problem is introduced to study worst-case attacks
that can cause a physical line over
ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation.
For attack classes (i) and (ii), both mathematical and experimental analyses sug-
gest that these unobservable attacks can be limited or even detected with resiliency
mechanisms including load monitoring, anomalous re-dispatches checking, and his-
torical data comparison. For attack class (iii), countermeasures including anomalous
tie-line interchange verication, anomalous re-dispatch alarms, and external contin-
gency lists sharing are needed to thwart such attacks.
transportation of power from the sources of power generation via an intermediate
densely connected transmission network to a large distribution network of end-users
at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-
mission/ distribution), the system is managed and monitored with a combination of
(a) supervisory control and data acquisition (SCADA); and (b) energy management
systems (EMSs) that process the collected data and make control and actuation de-
cisions using the collected data. However, at all levels of the hierarchy, both SCADA
and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the
electric power infrastructure, cyber attacks can have severe economic and social con-
sequences.
This thesis focuses on cyber attacks on SCADA and EMS at the transmission
level of the electric power system. The goal is to study the consequences of three
classes of cyber attacks that can change topology data. These classes include: (i)
unobservable state-preserving cyber attacks that only change the topology data; (ii)
unobservable state-and-topology cyber-physical attacks that change both states and
topology data to enable a coordinated physical and cyber attack; and (iii) topology-
targeted man-in-the-middle (MitM) communication attacks that alter topology data
shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on
the unobservable attacks on single regional EMS while class (iii) focuses on the MitM
attacks on communication links between regional EMSs. For each class of attacks,
the theoretical attack model and the implementation of attacks are provided, and the
worst-case attack and its consequences are exhaustively studied. In particularly, for
class (ii), a two-stage optimization problem is introduced to study worst-case attacks
that can cause a physical line over
ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation.
For attack classes (i) and (ii), both mathematical and experimental analyses sug-
gest that these unobservable attacks can be limited or even detected with resiliency
mechanisms including load monitoring, anomalous re-dispatches checking, and his-
torical data comparison. For attack class (iii), countermeasures including anomalous
tie-line interchange verication, anomalous re-dispatch alarms, and external contin-
gency lists sharing are needed to thwart such attacks.
ContributorsZhang, Jiazi (Author) / Sankar, Lalitha (Thesis advisor) / Hedman, Kory (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2015
Description
I propose a new communications scheme where signature signals are used to carry digital data by suitably modulating the signal parameters with information bits. One possible application for the proposed scheme is in underwater acoustic (UWA) communications; with this motivation, I demonstrate how it can be applied in UWA communications. In order to do that, I exploit existing parameterized models for mammalian sounds by using them as signature signals. Digital data is transmitted by mapping vectors of information bits to a carefully designed set of parameters with values obtained from the biomimetic signal models. To complete the overall system design, I develop appropriate receivers taking into account the specific UWA channel models. I present some numerical results from the analysis of data recorded during the Kauai Acomms MURI 2011 (KAM11) UWA communications experiment.
It is shown that the proposed communication scheme results in approximate channel models with amplitude-limited inputs and signal-dependent additive noise. Motivated by this observation, I study capacity of amplitude-limited channels under different transmission scenarios. Specifically, I consider fading channels, signal-dependent additive Gaussian noise channels, multiple-input multiple-output (MIMO) systems and parallel Gaussian channels under peak power constraints.
I also consider practical channel coding problems for channels with signal-dependent noise. I consider two specific models; signal-dependent additive Gaussian noise channels and Z-channels which serve as binary-input binary-output approximations to the Gaussian case. I propose a new upper bound on the probability of error, and utilize it for design of codes. I illustrate the tightness of the derived bounds and the performance of the designed codes via examples.
It is shown that the proposed communication scheme results in approximate channel models with amplitude-limited inputs and signal-dependent additive noise. Motivated by this observation, I study capacity of amplitude-limited channels under different transmission scenarios. Specifically, I consider fading channels, signal-dependent additive Gaussian noise channels, multiple-input multiple-output (MIMO) systems and parallel Gaussian channels under peak power constraints.
I also consider practical channel coding problems for channels with signal-dependent noise. I consider two specific models; signal-dependent additive Gaussian noise channels and Z-channels which serve as binary-input binary-output approximations to the Gaussian case. I propose a new upper bound on the probability of error, and utilize it for design of codes. I illustrate the tightness of the derived bounds and the performance of the designed codes via examples.
ContributorsElMoslimany, Ahmad (Author) / Duman, Tolga M. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2015