Matching Items (9)
Filtering by
- All Subjects: artificial intelligence
- All Subjects: Application software
- All Subjects: Policy Management
- Genre: Doctoral Dissertation
- Creators: Doupe, Adam
- Resource Type: Text
Description
Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging.
This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:
• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.
• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.
• Automatic code generation for MMAs: Investigating methods for automatically
• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.
This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:
• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.
• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.
• Automatic code generation for MMAs: Investigating methods for automatically
• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.
ContributorsBagade, Priyanka (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Wu, Carole-Jean (Committee member) / Doupe, Adam (Committee member) / Zhang, Yi (Committee member) / Arizona State University (Publisher)
Created2015
Description
The pervasive use of social media gives it a crucial role in helping the public perceive reliable information. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creation and dissemination of misinformation. It becomes increasingly difficult for online users to find accurate and trustworthy information. As witnessed in recent incidents of misinformation, it escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Different from some existing research in psychology and social sciences about misinformation, social media platforms pose unprecedented challenges for misinformation detection. First, intentional spreaders of misinformation will actively disguise themselves. Second, content of misinformation may be manipulated to avoid being detected, while abundant contextual information may play a vital role in detecting it. Third, not only accuracy, earliness of a detection method is also important in containing misinformation from being viral. Fourth, social media platforms have been used as a fundamental data source for various disciplines, and these research may have been conducted in the presence of misinformation. To tackle the challenges, we focus on developing machine learning algorithms that are robust to adversarial manipulation and data scarcity.
The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.
The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.
ContributorsWu, Liang (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / Doupe, Adam (Committee member) / Davison, Brian D. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption.
To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.
To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.
ContributorsJonas, Michael (Author) / Gaffar, Ashraf (Thesis advisor) / Fainekos, Georgios (Committee member) / Doupe, Adam (Committee member) / Herley, Cormac (Committee member) / Arizona State University (Publisher)
Created2016
Description
Software-Defined Networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane, which allows network administrators to consolidate common network services into a centralized module named SDN controller. Applications’ policies are transformed into standardized network rules in the data plane via SDN controller. Even though this centralization brings a great flexibility and programmability to the network, network rules generated by SDN applications cannot be trusted because there may exist malicious SDN applications, and insecure network flows can be made due to complex relations across network rules. In this dissertation, I investigate how to identify and resolve these security violations in SDN caused by the combination of network rules and applications’ policies. To this end, I propose a systematic policy management framework that better protects SDN itself and hardens existing network defense mechanisms using SDN.
More specifically, I discuss the following four security challenges in this dissertation: (1) In SDN, generating reliable network rules is challenging because SDN applications cannot be trusted and have complicated dependencies each other. To address this problem, I analyze applications’ policies and remove those dependencies by applying grid-based policy decomposition mechanism; (2) One network rule could accidentally affect others (or by malicious users), which lead to creating of indirect security violations. I build systematic and automated tools that analyze network rules in the data plane to detect a wide range of security violations and resolve them in an automated fashion; (3) A fundamental limitation of current SDN protocol (OpenFlow) is a lack of statefulness, which is extremely important to several security applications such as stateful firewall. To bring statelessness to SDN-based environment, I come up with an innovative stateful monitoring scheme by extending existing OpenFlow specifications; (4) Existing honeynet architecture is suffering from its limited functionalities of ’data control’ and ’data capture’. To address this challenge, I design and implement an innovative next generation SDN-based honeynet architecture.
More specifically, I discuss the following four security challenges in this dissertation: (1) In SDN, generating reliable network rules is challenging because SDN applications cannot be trusted and have complicated dependencies each other. To address this problem, I analyze applications’ policies and remove those dependencies by applying grid-based policy decomposition mechanism; (2) One network rule could accidentally affect others (or by malicious users), which lead to creating of indirect security violations. I build systematic and automated tools that analyze network rules in the data plane to detect a wide range of security violations and resolve them in an automated fashion; (3) A fundamental limitation of current SDN protocol (OpenFlow) is a lack of statefulness, which is extremely important to several security applications such as stateful firewall. To bring statelessness to SDN-based environment, I come up with an innovative stateful monitoring scheme by extending existing OpenFlow specifications; (4) Existing honeynet architecture is suffering from its limited functionalities of ’data control’ and ’data capture’. To address this challenge, I design and implement an innovative next generation SDN-based honeynet architecture.
ContributorsHan, Wonkyu (Author) / Ahn, Gail-Joon (Thesis advisor) / Zhao, Ziming (Thesis advisor) / Doupe, Adam (Committee member) / Huang, Dijiang (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2016
Description
While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature of distracted drivers and warning effectiveness, specifically by studying various warnings presented to drivers while they were operating a smart phone. Experiment One attempted to understand which smart phone tasks, (text vs image) or (self-paced vs other-paced) are the most distracting to a driver. Experiment Two compared the effectiveness of different smartphone based applications (app’s) for mitigating driver distraction. Experiment Three investigated the effects of informative auditory and tactile warnings which were designed to convey directional information to a distracted driver (moving towards or away). Lastly, Experiment Four extended the research into the area of autonomous driving by investigating the effectiveness of different auditory take-over request signals. Novel to both Experiment Three and Four was that the warnings were delivered from the source of the distraction (i.e., by either the sound triggered at the smart phone location or through a vibration given on the wrist of the hand holding the smart phone). This warning placement was an attempt to break the driver’s attentional focus on their smart phone and understand how to best re-orient the driver in order to improve the driver’s situational awareness (SA). The overall goal was to explore these novel methods of improved SA so drivers may more quickly and appropriately respond to a critical event.
ContributorsMcNabb, Jaimie Christine (Author) / Gray, Dr. Rob (Thesis advisor) / Branaghan, Dr. Russell (Committee member) / Becker, Dr. Vaughn (Committee member) / Arizona State University (Publisher)
Created2017
Description
The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.
ContributorsCheeks, Loretta H. (Author) / Gaffar, Ashraf (Thesis advisor) / Wald, Dara M (Committee member) / Ben Amor, Hani (Committee member) / Doupe, Adam (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Malicious hackers utilize the World Wide Web to share knowledge. Previous work has demonstrated that information mined from online hacking communities can be used as precursors to cyber-attacks. In a threatening scenario, where security alert systems are facing high false positive rates, understanding the people behind cyber incidents can help reduce the risk of attacks. However, the rapidly evolving nature of those communities leads to limitations still largely unexplored, such as: who are the skilled and influential individuals forming those groups, how they self-organize along the lines of technical expertise, how ideas propagate within them, and which internal patterns can signal imminent cyber offensives? In this dissertation, I have studied four key parts of this complex problem set. Initially, I leverage content, social network, and seniority analysis to mine key-hackers on darkweb forums, identifying skilled and influential individuals who are likely to succeed in their cybercriminal goals. Next, as hackers often use Web platforms to advertise and recruit collaborators, I analyze how social influence contributes to user engagement online. On social media, two time constraints are proposed to extend standard influence measures, which increases their correlation with adoption probability and consequently improves hashtag adoption prediction. On darkweb forums, the prediction of where and when hackers will post a message in the near future is accomplished by analyzing their recurrent interactions with other hackers. After that, I demonstrate how vendors of malware and malicious exploits organically form hidden organizations on darkweb marketplaces, obtaining significant consistency across the vendors’ communities extracted using the similarity of their products in different networks. Finally, I predict imminent cyber-attacks correlating malicious hacking activity on darkweb forums with real-world cyber incidents, evidencing how social indicators are crucial for the performance of the proposed model. This research is a hybrid of social network analysis (SNA), machine learning (ML), evolutionary computation (EC), and temporal logic (TL), presenting expressive contributions to empower cyber defense.
ContributorsSantana Marin, Ericsson (Author) / Shakarian, Paulo (Thesis advisor) / Doupe, Adam (Committee member) / Liu, Huan (Committee member) / Ferrara, Emilio (Committee member) / Arizona State University (Publisher)
Created2020
Description
Artificial Intelligence (AI) systems have achieved outstanding performance and have been found to be better than humans at various tasks, such as sentiment analysis, and face recognition. However, the majority of these state-of-the-art AI systems use complex Deep Learning (DL) methods which present challenges for human experts to design and evaluate such models with respect to privacy, fairness, and robustness. Recent examination of DL models reveals that representations may include information that could lead to privacy violations, unfairness, and robustness issues. This results in AI systems that are potentially untrustworthy from a socio-technical standpoint. Trustworthiness in AI is defined by a set of model properties such as non-discriminatory bias, protection of users’ sensitive attributes, and lawful decision-making. The characteristics of trustworthy AI can be grouped into three categories: Reliability, Resiliency, and Responsibility. Past research has shown that the successful integration of an AI model depends on its trustworthiness. Thus it is crucial for organizations and researchers to build trustworthy AI systems to facilitate the seamless integration and adoption of intelligent technologies. The main issue with existing AI systems is that they are primarily trained to improve technical measures such as accuracy on a specific task but are not considerate of socio-technical measures. The aim of this dissertation is to propose methods for improving the trustworthiness of AI systems through representation learning. DL models’ representations contain information about a given input and can be used for tasks such as detecting fake news on social media or predicting the sentiment of a review. The findings of this dissertation significantly expand the scope of trustworthy AI research and establish a new paradigm for modifying data representations to balance between properties of trustworthy AI. Specifically, this research investigates multiple techniques such as reinforcement learning for understanding trustworthiness in users’ privacy, fairness, and robustness in classification tasks like cyberbullying detection and fake news detection. Since most social measures in trustworthy AI cannot be used to fine-tune or train an AI model directly, the main contribution of this dissertation lies in using reinforcement learning to alter an AI system’s behavior based on non-differentiable social measures.
ContributorsMosallanezhad, Ahmadreza (Author) / Liu, Huan (Thesis advisor) / Mancenido, Michelle (Thesis advisor) / Doupe, Adam (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2023
Description
Recent advances in techniques allow the extraction of Cyber Threat Information (CTI) from online content, such as social media, blog articles, and posts in discussion forums. Most research work focuses on social media and blog posts since their content is often contributed by cybersecurity experts and is usually of cleaner formats. While posts in online forums are noisier and less structured, online forums attract more users than other sources and contain much valuable information that may help predict cyber threats. Therefore, effectively extracting CTI from online forum posts is an important task in today's data-driven cybersecurity defenses. Many Natural Language Processing (NLP) techniques are applied to the cybersecurity domains to extract the useful information, however, there is still space to improve. In this dissertation, a new Named Entity Recognition framework for cybersecurity domains and thread structure construction methods for unstructured forums are proposed to support the extraction of CTI. Then, extend them to filter the posts in the forums to eliminate non cybersecurity related topics with Cyber Attack Relevance Scale (CARS), extract the cybersecurity knowledgeable users to enhance more information for enhancing cybersecurity, and extract trending topic phrases related to cyber attacks in the hackers forums to find the clues for potential future attacks to predict them.
ContributorsKashihara, Kazuaki (Author) / Baral, Chitta (Thesis advisor) / Doupe, Adam (Committee member) / Blanco, Eduardo (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2022