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 21 - 30 of 38
Description
In traditional networks the control and data plane are highly coupled, hindering development. With Software Defined Networking (SDN), the two planes are separated, allowing innovations on either one independently of the other. Here, the control plane is formed by the applications that specify an organization's policy and the data plane contains the forwarding logic. The application sends all commands to an SDN controller which then performs the requested action on behalf of the application. Generally, the requested action is a modification to the flow tables, present in the switches, to reflect a change in the organization's policy. There are a number of ways to control the network using the SDN principles, but the most widely used approach is OpenFlow.
With the applications now having direct access to the flow table entries, it is easy to have inconsistencies arise in the flow table rules. Since the flow rules are structured similar to firewall rules, the research done in analyzing and identifying firewall rule conflicts can be adapted to work with OpenFlow rules.
The main work of this thesis is to implement flow conflict detection logic in OpenDaylight and inspect the applicability of techniques in visualizing the conflicts. A hierarchical edge-bundling technique coupled with a Reingold-Tilford tree is employed to present the relationship between the conflicting rules. Additionally, a table-driven approach is also implemented to display the details of each flow.
Both types of visualization are then tested for correctness by providing them with flows which are known to have conflicts. The conflicts were identified properly and displayed by the views.
With the applications now having direct access to the flow table entries, it is easy to have inconsistencies arise in the flow table rules. Since the flow rules are structured similar to firewall rules, the research done in analyzing and identifying firewall rule conflicts can be adapted to work with OpenFlow rules.
The main work of this thesis is to implement flow conflict detection logic in OpenDaylight and inspect the applicability of techniques in visualizing the conflicts. A hierarchical edge-bundling technique coupled with a Reingold-Tilford tree is employed to present the relationship between the conflicting rules. Additionally, a table-driven approach is also implemented to display the details of each flow.
Both types of visualization are then tested for correctness by providing them with flows which are known to have conflicts. The conflicts were identified properly and displayed by the views.
ContributorsNatarajan, Janakarajan (Author) / Huang, Dijiang (Thesis advisor) / Syrotiuk, Violet R. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2016
Description
Microblogging services such as Twitter, Sina Weibo, and Tumblr have been emerging and deeply embedded into people's daily lives. Used by hundreds of millions of users to connect the people worldwide and share and access information in real-time, the microblogging service has also became the target of malicious attackers due to its massive user engagement and structural openness. Although existed, little is still known in the community about new types of vulnerabilities in current microblogging services which could be leveraged by the intelligence-evolving attackers, and more importantly, the corresponding defenses that could prevent both the users and the microblogging service providers from being attacked. This dissertation aims to uncover a number of challenging security and privacy issues in microblogging services and also propose corresponding defenses.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
ContributorsZhang, Jinxue (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Ying, Lei (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2016
Description
The success of Bitcoin has generated significant interest in the financial community to understand whether the technological underpinnings of the cryptocurrency paradigm can be leveraged to improve the efficiency of financial processes in the existing infrastructure. Various alternative proposals, most notably, Ripple and Ethereum, aim to provide solutions to the financial community in different ways. These proposals derive their security guarantees from either the computational hardness of proof-of-work or voting based distributed consensus mechanism, both of which can be computationally expensive. Furthermore, the financial audit requirements for a participating financial institutions have not been suitably addressed.
This thesis presents a novel approach of constructing a non-consensus based decentralized financial transaction processing model with a built-in efficient audit structure. The problem of decentralized inter-bank payment processing is used for the model design. The two key insights used in this work are (1) to utilize a majority signature based replicated storage protocol for transaction authorization, and (2) to construct individual self-verifiable audit trails for each node as opposed to a common Blockchain. Theoretical analysis shows that the model provides cryptographic security for transaction processing and the presented audit structure facilitates financial auditing of individual nodes in time independent of the number of transactions.
This thesis presents a novel approach of constructing a non-consensus based decentralized financial transaction processing model with a built-in efficient audit structure. The problem of decentralized inter-bank payment processing is used for the model design. The two key insights used in this work are (1) to utilize a majority signature based replicated storage protocol for transaction authorization, and (2) to construct individual self-verifiable audit trails for each node as opposed to a common Blockchain. Theoretical analysis shows that the model provides cryptographic security for transaction processing and the presented audit structure facilitates financial auditing of individual nodes in time independent of the number of transactions.
ContributorsGupta, Saurabh (Author) / Bazzi, Rida (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Herlihy, Maurice (Committee member) / Arizona State University (Publisher)
Created2016
Description
Detecting cyber-attacks in cyber systems is essential for protecting cyber infrastructures from cyber-attacks. It is very difficult to detect cyber-attacks in cyber systems due to their high complexity. The accuracy of the attack detection in the cyber systems depends heavily on the completeness of the collected sensor information. In this thesis, two approaches are presented: one to detecting attacks in completely observable cyber systems, and the other to estimating types of states in partially observable cyber systems for attack detection in cyber systems. These two approaches are illustrated using three large data sets of network traffic because the packet-level information of the network traffic data provides details about the cyber systems.
The approach to attack detection in cyber systems is based on a multimodal artificial neural network (MANN) using the collected network traffic data from completely observable cyber systems for training and testing. Since the training of MANN is computationally intensive, to reduce the computational overhead, an efficient feature selection algorithm using the genetic algorithm is developed and incorporated in this approach.
In order to detect attacks in cyber systems in partially observable environments, an approach to estimating the types of states in partially observable cyber systems, which is the first phase of attack detection in cyber systems in partially observable environments, is presented. The types of states of such cyber systems are useful to detecting cyber-attacks in such cyber systems. This approach involves the use of a convolutional neural network (CNN), and unsupervised learning with elbow method and k-means clustering algorithm.
The approach to attack detection in cyber systems is based on a multimodal artificial neural network (MANN) using the collected network traffic data from completely observable cyber systems for training and testing. Since the training of MANN is computationally intensive, to reduce the computational overhead, an efficient feature selection algorithm using the genetic algorithm is developed and incorporated in this approach.
In order to detect attacks in cyber systems in partially observable environments, an approach to estimating the types of states in partially observable cyber systems, which is the first phase of attack detection in cyber systems in partially observable environments, is presented. The types of states of such cyber systems are useful to detecting cyber-attacks in such cyber systems. This approach involves the use of a convolutional neural network (CNN), and unsupervised learning with elbow method and k-means clustering algorithm.
ContributorsGuha, Sayantan (Author) / Yau, Stephen S. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2016
Description
Passwords are ubiquitous and are poised to stay that way due to their relative usability, security and deployability when compared with alternative authentication schemes. Unfortunately, humans struggle with some of the assumptions or requirements that are necessary for truly strong passwords. As administrators try to push users towards password complexity and diversity, users still end up using predictable mangling patterns on old passwords and reusing the same passwords across services; users even inadvertently converge on the same patterns to a surprising degree, making an attacker’s job easier. This work explores using machine learning techniques to pick out strong passwords from weak ones, from a dataset of 10 million passwords, based on how structurally similar they were to the rest of the set.
ContributorsTodd, Margaret Nicole (Author) / Xue, Guoliang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2016
Description
Mobile devices are penetrating everyday life. According to a recent Cisco report [10], the number of mobile connected devices such as smartphones, tablets, laptops, eReaders, and Machine-to-Machine (M2M) modules will hit 11.6 billion by 2021, exceeding the world's projected population at that time (7.8 billion). The rapid development of mobile devices has brought a number of emerging security and privacy issues in mobile computing. This dissertation aims to address a number of challenging security and privacy issues in mobile computing.
This dissertation makes fivefold contributions. The first and second parts study the security and privacy issues in Device-to-Device communications. Specifically, the first part develops a novel scheme to enable a new way of trust relationship called spatiotemporal matching in a privacy-preserving and efficient fashion. To enhance the secure communication among mobile users, the second part proposes a game-theoretical framework to stimulate the cooperative shared secret key generation among mobile users. The third and fourth parts investigate the security and privacy issues in mobile crowdsourcing. In particular, the third part presents a secure and privacy-preserving mobile crowdsourcing system which strikes a good balance among object security, user privacy, and system efficiency. The fourth part demonstrates a differentially private distributed stream monitoring system via mobile crowdsourcing. Finally, the fifth part proposes VISIBLE, a novel video-assisted keystroke inference framework that allows an attacker to infer a tablet user's typed inputs on the touchscreen by recording and analyzing the video of the tablet backside during the user's input process. Besides, some potential countermeasures to this attack are also discussed. This dissertation sheds the light on the state-of-the-art security and privacy issues in mobile computing.
This dissertation makes fivefold contributions. The first and second parts study the security and privacy issues in Device-to-Device communications. Specifically, the first part develops a novel scheme to enable a new way of trust relationship called spatiotemporal matching in a privacy-preserving and efficient fashion. To enhance the secure communication among mobile users, the second part proposes a game-theoretical framework to stimulate the cooperative shared secret key generation among mobile users. The third and fourth parts investigate the security and privacy issues in mobile crowdsourcing. In particular, the third part presents a secure and privacy-preserving mobile crowdsourcing system which strikes a good balance among object security, user privacy, and system efficiency. The fourth part demonstrates a differentially private distributed stream monitoring system via mobile crowdsourcing. Finally, the fifth part proposes VISIBLE, a novel video-assisted keystroke inference framework that allows an attacker to infer a tablet user's typed inputs on the touchscreen by recording and analyzing the video of the tablet backside during the user's input process. Besides, some potential countermeasures to this attack are also discussed. This dissertation sheds the light on the state-of-the-art security and privacy issues in mobile computing.
ContributorsSun, Jingchao (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Ying, Lei (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things (IoT). The steps from acquiring data and making a reactive decision base on the acquired sensor measurements are complex and requires careful execution of several steps. In many of these steps there are still technological gaps to fill that are due to the fact that several primitives that are desirable in a sensor network environment are bolt on the networks as application layer functionalities, rather than built in them. For several important functionalities that are at the core of IoT architectures we have developed a solution that is analyzed and discussed in the following chapters.
The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.
The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.
ContributorsGentz, Reinhard Werner (Author) / Scaglione, Anna (Thesis advisor) / Zhang, Yanchao (Committee member) / Peisert, Sean (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2017
Description
The ease of programmability in Software-Defined Networking (SDN) makes it a great platform for implementation of various initiatives that involve application deployment, dynamic topology changes, and decentralized network management in a multi-tenant data center environment. However, implementing security solutions in such an environment is fraught with policy conflicts and consistency issues with the hardness of this problem being affected by the distribution scheme for the SDN controllers.
In this dissertation, a formalism for flow rule conflicts in SDN environments is introduced. This formalism is realized in Brew, a security policy analysis framework implemented on an OpenDaylight SDN controller. Brew has comprehensive conflict detection and resolution modules to ensure that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free security policy implementation and preventing information leakage. Techniques for global prioritization of flow rules in a decentralized environment are presented, using which all SDN flow rule conflicts are recognized and classified. Strategies for unassisted resolution of these conflicts are also detailed. Alternately, if administrator input is desired to resolve conflicts, a novel visualization scheme is implemented to help the administrators view the conflicts in an aesthetic manner. The correctness, feasibility and scalability of the Brew proof-of-concept prototype is demonstrated. Flow rule conflict avoidance using a buddy address space management technique is studied as an alternate to conflict detection and resolution in highly dynamic cloud systems attempting to implement an SDN-based Moving Target Defense (MTD) countermeasures.
In this dissertation, a formalism for flow rule conflicts in SDN environments is introduced. This formalism is realized in Brew, a security policy analysis framework implemented on an OpenDaylight SDN controller. Brew has comprehensive conflict detection and resolution modules to ensure that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free security policy implementation and preventing information leakage. Techniques for global prioritization of flow rules in a decentralized environment are presented, using which all SDN flow rule conflicts are recognized and classified. Strategies for unassisted resolution of these conflicts are also detailed. Alternately, if administrator input is desired to resolve conflicts, a novel visualization scheme is implemented to help the administrators view the conflicts in an aesthetic manner. The correctness, feasibility and scalability of the Brew proof-of-concept prototype is demonstrated. Flow rule conflict avoidance using a buddy address space management technique is studied as an alternate to conflict detection and resolution in highly dynamic cloud systems attempting to implement an SDN-based Moving Target Defense (MTD) countermeasures.
ContributorsPisharody, Sandeep (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Syrotiuk, Violet (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2017
Description
Mobile telephony is a critical aspect of our modern society: through telephone calls,
it is possible to reach almost anyone around the globe. However, every mobile telephone
call placed implicitly leaks the user's location to the telephony service provider (TSP).
This privacy leakage is due to the fundamental nature of mobile telephony calls that
must connect to a local base station to receive service and place calls. Thus, the TSP
can track the physical location of the user for every call that they place. While the
The Internet is similar in this regard, privacy-preserving technologies such as Tor allow
users to connect to websites anonymously (without revealing to their ISP the site
that they are visiting). In this thesis, the scheme presented, called shadow calling,
to allow geolocation anonymous calling from legacy mobile devices. In this way,
the call is placed from the same number, however, the TSP will not know the user's
physical location. The scheme does not require any change on the network side and
can be used on current mobile networks. The scheme implemented is for the GSM
(commonly referred to as 2G) network, as it is the most widely used mode of mobile
telephony communication. The feasibility of our scheme is demonstrated through the
prototype. Shadow calling, which renders the users geolocation anonymous, will be
beneficial for users such as journalists, human rights activists in hostile nations, or
other privacy-demanding users.
it is possible to reach almost anyone around the globe. However, every mobile telephone
call placed implicitly leaks the user's location to the telephony service provider (TSP).
This privacy leakage is due to the fundamental nature of mobile telephony calls that
must connect to a local base station to receive service and place calls. Thus, the TSP
can track the physical location of the user for every call that they place. While the
The Internet is similar in this regard, privacy-preserving technologies such as Tor allow
users to connect to websites anonymously (without revealing to their ISP the site
that they are visiting). In this thesis, the scheme presented, called shadow calling,
to allow geolocation anonymous calling from legacy mobile devices. In this way,
the call is placed from the same number, however, the TSP will not know the user's
physical location. The scheme does not require any change on the network side and
can be used on current mobile networks. The scheme implemented is for the GSM
(commonly referred to as 2G) network, as it is the most widely used mode of mobile
telephony communication. The feasibility of our scheme is demonstrated through the
prototype. Shadow calling, which renders the users geolocation anonymous, will be
beneficial for users such as journalists, human rights activists in hostile nations, or
other privacy-demanding users.
ContributorsPinto, Gerard Lawrence (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Web applications are an incredibly important aspect of our modern lives. Organizations
and developers use automated vulnerability analysis tools, also known as
scanners, to automatically find vulnerabilities in their web applications during development.
Scanners have traditionally fallen into two types of approaches: black-box
and white-box. In the black-box approaches, the scanner does not have access to the
source code of the web application whereas a white-box approach has access to the
source code. Today’s state-of-the-art black-box vulnerability scanners employ various
methods to fuzz and detect vulnerabilities in a web application. However, these
scanners attempt to fuzz the web application with a number of known payloads and
to try to trigger a vulnerability. This technique is simple but does not understand
the web application that it is testing. This thesis, presents a new approach to vulnerability
analysis. The vulnerability analysis module presented uses a novel approach
of Inductive Reverse Engineering (IRE) to understand and model the web application.
IRE first attempts to understand the behavior of the web application by giving
certain number of input/output pairs to the web application. Then, the IRE module
hypothesizes a set of programs (in a limited language specific to web applications,
called AWL) that satisfy the input/output pairs. These hypotheses takes the form of
a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt
to detect vulnerabilities in this DAG. Further, it generates the payload based on the
DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability
(based on our understanding of the program). It then tests this potential
vulnerability using the generated payload on the actual web application, and creates
a verification procedure to see if the potential vulnerability is actually vulnerable,
based on the web application’s response.
and developers use automated vulnerability analysis tools, also known as
scanners, to automatically find vulnerabilities in their web applications during development.
Scanners have traditionally fallen into two types of approaches: black-box
and white-box. In the black-box approaches, the scanner does not have access to the
source code of the web application whereas a white-box approach has access to the
source code. Today’s state-of-the-art black-box vulnerability scanners employ various
methods to fuzz and detect vulnerabilities in a web application. However, these
scanners attempt to fuzz the web application with a number of known payloads and
to try to trigger a vulnerability. This technique is simple but does not understand
the web application that it is testing. This thesis, presents a new approach to vulnerability
analysis. The vulnerability analysis module presented uses a novel approach
of Inductive Reverse Engineering (IRE) to understand and model the web application.
IRE first attempts to understand the behavior of the web application by giving
certain number of input/output pairs to the web application. Then, the IRE module
hypothesizes a set of programs (in a limited language specific to web applications,
called AWL) that satisfy the input/output pairs. These hypotheses takes the form of
a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt
to detect vulnerabilities in this DAG. Further, it generates the payload based on the
DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability
(based on our understanding of the program). It then tests this potential
vulnerability using the generated payload on the actual web application, and creates
a verification procedure to see if the potential vulnerability is actually vulnerable,
based on the web application’s response.
ContributorsKhairnar, Tejas (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017