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Security of Autonomous Systems under Physical Attacks: With application to Self-Driving Cars
Title
Security of Autonomous Systems under Physical Attacks: With application to Self-Driving Cars.
Creator
Dutta, Raj, Jin, Yier, Sundaram, Kalpathy, DeMara, Ronald, Zhang, Shaojie, Zhang, Teng, University of Central Florida
Abstract / Description
The drive to achieve trustworthy autonomous cyber-physical systems (CPS), which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for fulfilling these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system,...
Show moreThe drive to achieve trustworthy autonomous cyber-physical systems (CPS), which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for fulfilling these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system, uncertainties can arise from the operating environment, adversarial attacks, and from within the system. As a result of these concerns, human-beings lack trust in these systems and hesitate to use them for day-to-day use.In this dissertation, we develop algorithms to enhance trust by mitigating physical attacks targeting the integrity and security of sensing units of autonomous CPS. The sensors of these systems are responsible for gathering data of the physical processes. Lack of measures for securing their information can enable malicious attackers to cause life-threatening situations. This serves as a motivation for developing attack resilient solutions.Among various security solutions, attention has been recently paid toward developing system-level countermeasures for CPS whose sensor measurements are corrupted by an attacker. Our methods are along this direction as we develop an active and multiple passive algorithm to detect the attack and minimize its effect on the internal state estimates of the system. In the active approach, we leverage a challenge authentication technique for detection of two types of attacks: The Denial of Service (DoS) and the delay injection on active sensors of the systems. Furthermore, we develop a recursive least square estimator for recovery of system from attacks. The majority of the dissertation focuses on designing passive approaches for sensor attacks. In the first method, we focus on a linear stochastic system with multiple sensors, where measurements are fused in a central unit to estimate the state of the CPS. By leveraging Bayesian interpretation of the Kalman filter and combining it with the Chi-Squared detector, we recursively estimate states within an error bound and detect the DoS and False Data Injection attacks. We also analyze the asymptotic performance of the estimator and provide conditions for resilience of the state estimate.Next, we propose a novel distributed estimator based on l1 norm optimization, which could recursively estimate states within an error bound without restricting the number of agents of the distributed system that can be compromised. We also extend this estimator to a vehicle platoon scenario which is subjected to sparse attacks. Furthermore, we analyze the resiliency and asymptotic properties of both the estimators. Finally, at the end of the dissertation, we make an initial effort to formally verify the control system of the autonomous CPS using the statistical model checking method. It is done to ensure that a real-time and resource constrained system such as a self-driving car, with controllers and security solutions, adheres to strict timing constrains.
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Date Issued
2018
Identifier
CFE0007174, ucf:52253
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007174
Solution of linear ill-posed problems using overcomplete dictionaries
Title
Solution of linear ill-posed problems using overcomplete dictionaries.
Creator
Gupta, Pawan, Pensky, Marianna, Swanson, Jason, Zhang, Teng, Foroosh, Hassan, University of Central Florida
Abstract / Description
In this dissertation, we consider an application of overcomplete dictionaries to the solution of general ill-posed linear inverse problems. In the context of regression problems, there has been an enormous amount of effort to recover an unknown function using such dictionaries. While some research on the subject has been already carried out, there are still many gaps to address. In particular, one of the most popular methods, lasso, and its variants, is based on minimizing the empirical...
Show moreIn this dissertation, we consider an application of overcomplete dictionaries to the solution of general ill-posed linear inverse problems. In the context of regression problems, there has been an enormous amount of effort to recover an unknown function using such dictionaries. While some research on the subject has been already carried out, there are still many gaps to address. In particular, one of the most popular methods, lasso, and its variants, is based on minimizing the empirical likelihood and unfortunately, requires stringent assumptions on the dictionary, the so-called, compatibility conditions. Though compatibility conditions are hard to satisfy, it is well known that this can be accomplished by using random dictionaries. In the first part of the dissertation, we show how one can apply random dictionaries to the solution of ill-posed linear inverse problems with Gaussian noise. We put a theoretical foundation under the suggested methodology and study its performance via simulations and real-data example. In the second part of the dissertation, we investigate the application of lasso to the linear ill-posed problems with non-Gaussian noise. We have developed a theoretical background for the application of lasso to such problems and studied its performance via simulations.
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Date Issued
2019
Identifier
CFE0007811, ucf:52345
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007811
Modeling Disease Impact of Vibrio-Phage Interactions
Title
Modeling Disease Impact of Vibrio-Phage Interactions.
Creator
Botelho, Christopher, Shuai, Zhisheng, Nevai, A, Zhang, Teng, Teter, Kenneth, University of Central Florida
Abstract / Description
Since the work of John Snow, scientists and medical professionals have understood that individuals develop cholera by means of consuming contaminated water. Despite the knowledge(&)nbsp;of cholera's route of infection, many countries have experienced and still experience endemic cholera. Cholera is caused by the Vibrio cholerae (V. cholerae) bacterium and presents with acute diarrhea and vomiting. If untreated, infected individuals may die due to dehydration. Cholera is a disease that most...
Show moreSince the work of John Snow, scientists and medical professionals have understood that individuals develop cholera by means of consuming contaminated water. Despite the knowledge(&)nbsp;of cholera's route of infection, many countries have experienced and still experience endemic cholera. Cholera is caused by the Vibrio cholerae (V. cholerae) bacterium and presents with acute diarrhea and vomiting. If untreated, infected individuals may die due to dehydration. Cholera is a disease that most commonly affects countries with poor infrastructure and water sanitation. Despite efforts to control cholera in such countries, the disease persists. One such example is Haiti which has been experiencing a cholera outbreak since 2010. While there has been much research in the field of microbiology to understand V. cholerae, there has been comparably less research in the field of mathematical biology to understand the dynamics of V. cholerae in the environment. A mathematical model of V. cholerae incorporating a phage population is coupled with a SIRS disease model to examine the impact of vibrio and phage interaction. It is shown that there might exist two endemic equilibria, besides the disease free equilibrium: one in which phage persist in the environment and one in which the phage fail to persist. Existence and stability of these equilibria are established. Disease control strategies based on vibrio and phage interactions are discussed.
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Date Issued
2019
Identifier
CFE0007604, ucf:52544
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007604
Describing Images by Semantic Modeling using Attributes and Tags
Title
Describing Images by Semantic Modeling using Attributes and Tags.
Creator
Mahmoudkalayeh, Mahdi, Shah, Mubarak, Sukthankar, Gita, Rahnavard, Nazanin, Zhang, Teng, University of Central Florida
Abstract / Description
This dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the...
Show moreThis dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the changes in training data, naturally solves the problem of feature fusion and handles the challenge of the rare tags. Unlike tags, attributes are category-agnostic, hence their combination models an exponential number of semantic labels. Motivated by the fact that most attributes describe local properties, we propose exploiting localization cues, through semantic parsing of human face and body to improve person-related attribute prediction. We also demonstrate that image-level attribute labels can be effectively used as weak supervision for the task of semantic segmentation. Next, we analyze the Selfie images by utilizing tags and attributes. We collect the first large-scale Selfie dataset and annotate it with different attributes covering characteristics such as gender, age, race, facial gestures, and hairstyle. We then study the popularity and sentiments of the selfies given an estimated appearance of various semantic concepts. In brief, we automatically infer what makes a good selfie. Despite its extensive usage, the deep learning literature falls short in understanding the characteristics and behavior of the Batch Normalization. We conclude this dissertation by providing a fresh view, in light of information geometry and Fisher kernels to why the batch normalization works. We propose Mixture Normalization that disentangles modes of variation in the underlying distribution of the layer outputs and confirm that it effectively accelerates training of different batch-normalized architectures including Inception-V3, Densely Connected Networks, and Deep Convolutional Generative Adversarial Networks while achieving better generalization error.
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Date Issued
2019
Identifier
CFE0007493, ucf:52640
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007493
Estimation and clustering in statistical ill-posed linear inverse problems
Title
Estimation and clustering in statistical ill-posed linear inverse problems.
Creator
Rajapakshage, Rasika, Pensky, Marianna, Swanson, Jason, Zhang, Teng, Bagci, Ulas, Foroosh, Hassan, University of Central Florida
Abstract / Description
The main focus of the dissertation is estimation and clustering in statistical ill-posed linear inverse problems. The dissertation deals with a problem of simultaneously estimating a collection of solutions of ill-posed linear inverse problems from their noisy images under an operator that does not have a bounded inverse, when the solutions are related in a certain way. The dissertation defense consists of three parts. In the first part, the collection consists of measurements of temporal...
Show moreThe main focus of the dissertation is estimation and clustering in statistical ill-posed linear inverse problems. The dissertation deals with a problem of simultaneously estimating a collection of solutions of ill-posed linear inverse problems from their noisy images under an operator that does not have a bounded inverse, when the solutions are related in a certain way. The dissertation defense consists of three parts. In the first part, the collection consists of measurements of temporal functions at various spatial locations. In particular, we studythe problem of estimating a three-dimensional function based on observations of its noisy Laplace convolution. In the second part, we recover classes of similar curves when the class memberships are unknown. Problems of this kind appear in many areas of application where clustering is carried out at the pre-processing step and then the inverse problem is solved for each of the cluster averages separately. As a result, the errors of the procedures are usually examined for the estimation step only. In both parts, we construct the estimators, study their minimax optimality and evaluate their performance via a limited simulation study. In the third part, we propose a new computational platform to better understand the patterns of R-fMRI by taking into account the challenge of inevitable signal fluctuations and interpretthe success of dynamic functional connectivity approaches. Towards this, we revisit an auto-regressive and vector auto-regressive signal modeling approach for estimating temporal changes of the signal in brain regions. We then generate inverse covariance matrices fromthe generated windows and use a non-parametric statistical approach to select significant features. Finally, we use Lasso to perform classification of the data. The effectiveness of theproposed method is evidenced in the classification of R-fMRI scans
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Date Issued
2019
Identifier
CFE0007710, ucf:52450
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007710