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- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Access control is necessary for information assurance in many of today's applications such as banking and electronic health record. Access control breaches are critical security problems that can result from unintended and improper implementation of security policies. Security testing can help identify security vulnerabilities early and avoid unexpected expensive cost in handling breaches for security architects and security engineers. The process of security testing which involves creating tests that effectively examine vulnerabilities is a challenging task. Role-Based Access Control (RBAC) has been widely adopted to support fine-grained access control. However, in practice, due to its complexity including role management, role hierarchy with hundreds of roles, and their associated privileges and users, systematically testing RBAC systems is crucial to ensure the security in various domains ranging from cyber-infrastructure to mission-critical applications. In this thesis, we introduce i) a security testing technique for RBAC systems considering the principle of maximum privileges, the structure of the role hierarchy, and a new security test coverage criterion; ii) a MTBDD (Multi-Terminal Binary Decision Diagram) based representation of RBAC security policy including RHMTBDD (Role Hierarchy MTBDD) to efficiently generate effective positive and negative security test cases; and iii) a security testing framework which takes an XACML-based RBAC security policy as an input, parses it into a RHMTBDD representation and then generates positive and negative test cases. We also demonstrate the efficacy of our approach through case studies.
ContributorsGupta, Poonam (Author) / Ahn, Gail-Joon (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this dissertation, two interrelated problems of service-based systems (SBS) are addressed: protecting users' data confidentiality from service providers, and managing performance of multiple workflows in SBS. Current SBSs pose serious limitations to protecting users' data confidentiality. Since users' sensitive data is sent in unencrypted forms to remote machines owned and operated by third-party service providers, there are risks of unauthorized use of the users' sensitive data by service providers. Although there are many techniques for protecting users' data from outside attackers, currently there is no effective way to protect users' sensitive data from service providers. In this dissertation, an approach is presented to protecting the confidentiality of users' data from service providers, and ensuring that service providers cannot collect users' confidential data while the data is processed or stored in cloud computing systems. The approach has four major features: (1) separation of software service providers and infrastructure service providers, (2) hiding the information of the owners of data, (3) data obfuscation, and (4) software module decomposition and distributed execution. Since the approach to protecting users' data confidentiality includes software module decomposition and distributed execution, it is very important to effectively allocate the resource of servers in SBS to each of the software module to manage the overall performance of workflows in SBS. An approach is presented to resource allocation for SBS to adaptively allocating the system resources of servers to their software modules in runtime in order to satisfy the performance requirements of multiple workflows in SBS. Experimental results show that the dynamic resource allocation approach can substantially increase the throughput of a SBS and the optimal resource allocation can be found in polynomial time
ContributorsAn, Ho Geun (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Ahn, Gail-Joon (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2012
Description
The telephone network is used by almost every person in the modern world. With the rise of Internet access to the PSTN, the telephone network today is rife with telephone spam and scams. Spam calls are significant annoyances for telephone users, unlike email spam, spam calls demand immediate attention. They are not only significant annoyances but also result in significant financial losses in the economy. According to complaint data from the FTC, complaints on illegal calls have made record numbers in recent years. Americans lose billions to fraud due to malicious telephone communication, despite various efforts to subdue telephone spam, scam, and robocalls.
In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.
In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.
ContributorsTu, Huahong (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Thesis advisor) / Huang, Dijiang (Committee member) / Zhang, Yanchao (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
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
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
Cyber systems, including IoT (Internet of Things), are increasingly being used ubiquitously to vastly improve the efficiency and reduce the cost of critical application areas, such as finance, transportation, defense, and healthcare. Over the past two decades, computing efficiency and hardware cost have dramatically been improved. These improvements have made cyber systems omnipotent, and control many aspects of human lives. Emerging trends in successful cyber system breaches have shown increasing sophistication in attacks and that attackers are no longer limited by resources, including human and computing power. Most existing cyber defense systems for IoT systems have two major issues: (1) they do not incorporate human user behavior(s) and preferences in their approaches, and (2) they do not continuously learn from dynamic environment and effectively adapt to thwart sophisticated cyber-attacks. Consequently, the security solutions generated may not be usable or implementable by the user(s) thereby drastically reducing the effectiveness of these security solutions.
In order to address these major issues, a comprehensive approach to securing ubiquitous smart devices in IoT environment by incorporating probabilistic human user behavioral inputs is presented. The approach will include techniques to (1) protect the controller device(s) [smart phone or tablet] by continuously learning and authenticating the legitimate user based on the touch screen finger gestures in the background, without requiring users’ to provide their finger gesture inputs intentionally for training purposes, and (2) efficiently configure IoT devices through controller device(s), in conformance with the probabilistic human user behavior(s) and preferences, to effectively adapt IoT devices to the changing environment. The effectiveness of the approach will be demonstrated with experiments that are based on collected user behavioral data and simulations.
In order to address these major issues, a comprehensive approach to securing ubiquitous smart devices in IoT environment by incorporating probabilistic human user behavioral inputs is presented. The approach will include techniques to (1) protect the controller device(s) [smart phone or tablet] by continuously learning and authenticating the legitimate user based on the touch screen finger gestures in the background, without requiring users’ to provide their finger gesture inputs intentionally for training purposes, and (2) efficiently configure IoT devices through controller device(s), in conformance with the probabilistic human user behavior(s) and preferences, to effectively adapt IoT devices to the changing environment. The effectiveness of the approach will be demonstrated with experiments that are based on collected user behavioral data and simulations.
ContributorsBuduru, Arun Balaji (Author) / Yau, Sik-Sang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2016