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.
Displaying 1 - 4 of 4
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- All Subjects: Computer networks--Security measures.
- All Subjects: High-Dimensional
- Creators: Davulcu, Hasan
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures.
ContributorsNagaraja, Vinjith (Author) / Yau, Stephen S. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The increasing usage of smart-phones and mobile devices in work environment and IT
industry has brought about unique set of challenges and opportunities. ARM architecture
in particular has evolved to a point where it supports implementations across wide spectrum
of performance points and ARM based tablets and smart-phones are in demand. The
enhancements to basic ARM RISC architecture allow ARM to have high performance,
small code size, low power consumption and small silicon area. Users want their devices to
perform many tasks such as read email, play games, and run other online applications and
organizations no longer desire to provision and maintain individual’s IT equipment. The
term BYOD (Bring Your Own Device) has come into being from demand of such a work
setup and is one of the motivation of this research work. It brings many opportunities such
as increased productivity and reduced costs and challenges such as secured data access,
data leakage and amount of control by the organization.
To provision such a framework we need to bridge the gap from both organizations side
and individuals point of view. Mobile device users face issue of application delivery on
multiple platforms. For instance having purchased many applications from one proprietary
application store, individuals may want to move them to a different platform/device but
currently this is not possible. Organizations face security issues in providing such a solution
as there are many potential threats from allowing BYOD work-style such as unauthorized
access to data, attacks from the devices within and outside the network.
ARM based Secure Mobile SDN framework will resolve these issues and enable employees
to consolidate both personal and business calls and mobile data access on a single device.
To address application delivery issue we are introducing KVM based virtualization that
will allow host OS to run multiple guest OS. To address the security problem we introduce
SDN environment where host would be running bridged network of guest OS using Open
vSwitch . This would allow a remote controller to monitor the state of guest OS for making
important control and traffic flow decisions based on the situation.
industry has brought about unique set of challenges and opportunities. ARM architecture
in particular has evolved to a point where it supports implementations across wide spectrum
of performance points and ARM based tablets and smart-phones are in demand. The
enhancements to basic ARM RISC architecture allow ARM to have high performance,
small code size, low power consumption and small silicon area. Users want their devices to
perform many tasks such as read email, play games, and run other online applications and
organizations no longer desire to provision and maintain individual’s IT equipment. The
term BYOD (Bring Your Own Device) has come into being from demand of such a work
setup and is one of the motivation of this research work. It brings many opportunities such
as increased productivity and reduced costs and challenges such as secured data access,
data leakage and amount of control by the organization.
To provision such a framework we need to bridge the gap from both organizations side
and individuals point of view. Mobile device users face issue of application delivery on
multiple platforms. For instance having purchased many applications from one proprietary
application store, individuals may want to move them to a different platform/device but
currently this is not possible. Organizations face security issues in providing such a solution
as there are many potential threats from allowing BYOD work-style such as unauthorized
access to data, attacks from the devices within and outside the network.
ARM based Secure Mobile SDN framework will resolve these issues and enable employees
to consolidate both personal and business calls and mobile data access on a single device.
To address application delivery issue we are introducing KVM based virtualization that
will allow host OS to run multiple guest OS. To address the security problem we introduce
SDN environment where host would be running bridged network of guest OS using Open
vSwitch . This would allow a remote controller to monitor the state of guest OS for making
important control and traffic flow decisions based on the situation.
ContributorsChowdhary, Ankur (Author) / Huang, Dijiang (Thesis advisor) / Tong, Hanghang (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Similarity search in high-dimensional spaces is popular for applications like image
processing, time series, and genome data. In higher dimensions, the phenomenon of
curse of dimensionality kills the effectiveness of most of the index structures, giving
way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity
searches. In addition to range searches and k-nearest neighbor searches, there
is a need to answer negative queries formed by excluded regions, in high-dimensional
data. Though there have been a slew of variants of LSH to improve efficiency, reduce
storage, and provide better accuracies, none of the techniques are capable of
answering queries in the presence of excluded regions.
This thesis provides a novel approach to handle such negative queries. This is
achieved by creating a prefix based hierarchical index structure. First, the higher
dimensional space is projected to a lower dimension space. Then, a one-dimensional
ordering is developed, while retaining the hierarchical traits. The algorithm intelligently
prunes the irrelevant candidates while answering queries in the presence of
excluded regions. While naive LSH would need to filter out the negative query results
from the main results, the new algorithm minimizes the need to fetch the redundant
results in the first place. Experiment results show that this reduces post-processing
cost thereby reducing the query processing time.
processing, time series, and genome data. In higher dimensions, the phenomenon of
curse of dimensionality kills the effectiveness of most of the index structures, giving
way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity
searches. In addition to range searches and k-nearest neighbor searches, there
is a need to answer negative queries formed by excluded regions, in high-dimensional
data. Though there have been a slew of variants of LSH to improve efficiency, reduce
storage, and provide better accuracies, none of the techniques are capable of
answering queries in the presence of excluded regions.
This thesis provides a novel approach to handle such negative queries. This is
achieved by creating a prefix based hierarchical index structure. First, the higher
dimensional space is projected to a lower dimension space. Then, a one-dimensional
ordering is developed, while retaining the hierarchical traits. The algorithm intelligently
prunes the irrelevant candidates while answering queries in the presence of
excluded regions. While naive LSH would need to filter out the negative query results
from the main results, the new algorithm minimizes the need to fetch the redundant
results in the first place. Experiment results show that this reduces post-processing
cost thereby reducing the query processing time.
ContributorsBhat, Aneesha (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2016