Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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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 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
Description
Access control has been historically recognized as an effective technique for ensuring that computer systems preserve important security properties. Recently, attribute-based
access control (ABAC) has emerged as a new paradigm to provide access mediation
by leveraging the concept of attributes: observable properties that become relevant under a certain security context and are exhibited by the entities normally involved in the mediation process, namely, end-users and protected resources. Also recently, independently-run organizations from the private and public sectors have recognized the benefits of engaging in multi-disciplinary research collaborations that involve sharing sensitive proprietary resources such as scientific data, networking capabilities and computation time and have recognized ABAC as the paradigm that suits their needs for restricting the way such resources are to be shared with each other. In such a setting, a robust yet flexible access mediation scheme is crucial to guarantee participants are granted access to such resources in a safe and secure manner.
However, no consensus exists either in the literature with respect to a formal model that clearly defines the way the components depicted in ABAC should interact with each other, so that the rigorous study of security properties to be effectively pursued. This dissertation proposes an approach tailored to provide a well-defined and formal definition of ABAC, including a description on how attributes exhibited by different independent organizations are to be leveraged for mediating access to shared resources, by allowing for collaborating parties to engage in federations for the specification, discovery, evaluation and communication of attributes, policies, and access mediation decisions. In addition, a software assurance framework is introduced to support the correct construction of enforcement mechanisms implementing our approach by leveraging validation and verification techniques based on software assertions, namely, design by contract (DBC) and behavioral interface specification languages (BISL). Finally, this dissertation also proposes a distributed trust framework that allows for exchanging recommendations on the perceived reputations of members of our proposed federations, in such a way that the level of trust of previously-unknown participants can be properly assessed for the purposes of access mediation.
access control (ABAC) has emerged as a new paradigm to provide access mediation
by leveraging the concept of attributes: observable properties that become relevant under a certain security context and are exhibited by the entities normally involved in the mediation process, namely, end-users and protected resources. Also recently, independently-run organizations from the private and public sectors have recognized the benefits of engaging in multi-disciplinary research collaborations that involve sharing sensitive proprietary resources such as scientific data, networking capabilities and computation time and have recognized ABAC as the paradigm that suits their needs for restricting the way such resources are to be shared with each other. In such a setting, a robust yet flexible access mediation scheme is crucial to guarantee participants are granted access to such resources in a safe and secure manner.
However, no consensus exists either in the literature with respect to a formal model that clearly defines the way the components depicted in ABAC should interact with each other, so that the rigorous study of security properties to be effectively pursued. This dissertation proposes an approach tailored to provide a well-defined and formal definition of ABAC, including a description on how attributes exhibited by different independent organizations are to be leveraged for mediating access to shared resources, by allowing for collaborating parties to engage in federations for the specification, discovery, evaluation and communication of attributes, policies, and access mediation decisions. In addition, a software assurance framework is introduced to support the correct construction of enforcement mechanisms implementing our approach by leveraging validation and verification techniques based on software assertions, namely, design by contract (DBC) and behavioral interface specification languages (BISL). Finally, this dissertation also proposes a distributed trust framework that allows for exchanging recommendations on the perceived reputations of members of our proposed federations, in such a way that the level of trust of previously-unknown participants can be properly assessed for the purposes of access mediation.
ContributorsRubio Medrano, Carlos Ernesto (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Zhao, Ziming (Committee member) / Santanam, Raghu (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2016
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