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Description
The 21st-century professional or knowledge worker spends much of the working day engaging others through electronic communication. The modes of communication available to knowledge workers have rapidly increased due to computerized technology advances: conference and video calls, instant messaging, e-mail, social media, podcasts, audio books, webinars, and much more. Professionals

The 21st-century professional or knowledge worker spends much of the working day engaging others through electronic communication. The modes of communication available to knowledge workers have rapidly increased due to computerized technology advances: conference and video calls, instant messaging, e-mail, social media, podcasts, audio books, webinars, and much more. Professionals who think for a living express feelings of stress about their ability to respond and fear missing critical tasks or information as they attempt to wade through all the electronic communication that floods their inboxes. Although many electronic communication tools compete for the attention of the contemporary knowledge worker, most professionals use an electronic personal information management (PIM) system, more commonly known as an e-mail application and often the ubiquitous Microsoft Outlook program. The aim of this research was to provide knowledge workers with solutions to manage the influx of electronic communication that arrives daily by studying the workers in their working environment. This dissertation represents a quest to understand the current strategies knowledge workers use to manage their e-mail, and if modification of e-mail management strategies can have an impact on productivity and stress levels for these professionals. Today’s knowledge workers rarely work entirely alone, justifying the importance of also exploring methods to improve electronic communications within teams.
ContributorsCounts, Virginia (Author) / Parrish, Kristen (Thesis advisor) / Allenby, Braden (Thesis advisor) / Landis, Amy (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Reasoning about the activities of cyber threat actors is critical to defend against cyber

attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult

to determine who the attacker is, what the desired goals are of the attacker, and how they will

carry out their attacks.

Reasoning about the activities of cyber threat actors is critical to defend against cyber

attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult

to determine who the attacker is, what the desired goals are of the attacker, and how they will

carry out their attacks. These three questions essentially entail understanding the attacker’s

use of deception, the capabilities available, and the intent of launching the attack. These

three issues are highly inter-related. If an adversary can hide their intent, they can better

deceive a defender. If an adversary’s capabilities are not well understood, then determining

what their goals are becomes difficult as the defender is uncertain if they have the necessary

tools to accomplish them. However, the understanding of these aspects are also mutually

supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we

understand intent and capabilities, a defender may be able to see through deception schemes.

In this dissertation, I present three pieces of work to tackle these questions to obtain

a better understanding of cyber threats. First, we introduce a new reasoning framework

to address deception. We evaluate the framework by building a dataset from DEFCON

capture-the-flag exercise to identify the person or group responsible for a cyber attack.

We demonstrate that the framework not only handles cases of deception but also provides

transparent decision making in identifying the threat actor. The second task uses a cognitive

learning model to determine the intent – goals of the threat actor on the target system.

The third task looks at understanding the capabilities of threat actors to target systems by

identifying at-risk systems from hacker discussions on darkweb websites. To achieve this

task we gather discussions from more than 300 darkweb websites relating to malicious

hacking.
ContributorsNunes, Eric (Author) / Shakarian, Paulo (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
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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.

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.
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
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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

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.
ContributorsJonas, Michael (Author) / Gaffar, Ashraf (Thesis advisor) / Fainekos, Georgios (Committee member) / Doupe, Adam (Committee member) / Herley, Cormac (Committee member) / Arizona State University (Publisher)
Created2016
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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

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
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Description
This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that

This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions.
ContributorsDemir, Mustafa, Ph.D (Author) / Cooke, Nancy J. (Thesis advisor) / Bekki, Jennifer (Committee member) / Amazeen, Polemnia G (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
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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

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
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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

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
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Description
It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI

It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI agents are integrated into the team, the first study asks what roles can AI agents take? The first study investigates this issue by asking whether an AI agent can take the role of a facilitator and in turn, improve planning outcomes by facilitating team processes. Results indicate that the human facilitator was significantly better than the AI facilitator at reducing cognitive biases such as groupthink, anchoring, and information pooling, as well as increasing decision quality and score. Additionally, participants viewed the AI facilitator negatively and ignored its inputs compared to the human facilitator. Yet, participants in the AI Facilitator condition performed significantly better than participants in the No Facilitator condition, illustrating that having an AI facilitator was better than having no facilitator at all. The second study explores whether artificial social intelligence (ASI) agents can take the role of advisors and subsequently improve team processes and mission outcome during a simulated search-and-rescue mission. The results of this study indicate that although ASI advisors can successfully advise teams, they also use a significantly greater number of taskwork interventions than teamwork interventions. Additionally, this study served to identify what the ASI advisors got right compared to the human advisor and vice versa. Implications and future directions are discussed.
ContributorsBuchanan, Verica (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S. (Committee member) / Roscoe, Rod D. (Committee member) / Arizona State University (Publisher)
Created2023
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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 hel

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