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.
Displaying 1 - 4 of 4
Filtering by
- Creators: Wang, Ruoyu
Description
Reverse engineering is a process focused on gaining an understanding for the intricaciesof a system. This practice is critical in cybersecurity as it promotes the
findings and patching of vulnerabilities as well as the counteracting of malware. Disassemblers
and decompilers have become essential when reverse engineering due to
the readability of information they transcribe from binary files. However, these tools
still tend to produce involved and complicated outputs that hinder the acquisition of
knowledge during binary analysis. Cognitive Load Theory (CLT) explains that this
hindrance is due to the human brain’s inability to process superfluous amounts of
data. CLT classifies this data into three cognitive load types — intrinsic, extraneous,
and germane — that each can help gauge complex procedures.
In this research paper, a novel program call graph is presented accounting for
these CLT principles. The goal of this graphical view is to reduce the cognitive load
tied to the depiction of binary information and to enhance the overall binary analysis
process. This feature was implemented within the binary analysis tool, angr and it’s
user interface counterpart, angr-management. Additionally, this paper will examine a
conducted user study to quantitatively and qualitatively evaluate the effectiveness of
the newly proposed proximity view (PV). The user study includes a binary challenge
solving portion measured by defined metrics and a survey phase to receive direct participant
feedback regarding the view. The results from this study show statistically
significant evidence that PV aids in challenge solving and improves the overall understanding
binaries. The results also signify that this improvement comes with the
cost of time. The survey section of the user study further indicates that users find
PV beneficial to the reverse engineering process, but additional information needs to
be included in future developments.
ContributorsSmits, Sean (Author) / Wang, Ruoyu (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2022
Description
Binary analysis and software debugging are critical tools in the modern softwaresecurity ecosystem. With the security arms race between attackers discovering and
exploiting vulnerabilities and the development teams patching bugs ever-tightening,
there is an immense need for more tooling to streamline the binary analysis and
debugging processes. Whether attempting to find the root cause for a buffer overflow
or a segmentation fault, the analysis process often involves manually tracing the
movement of data throughout a program’s life cycle. Up until this point, there has
not been a viable solution to the human limitation of maintaining a cohesive mental
image of the intricacies of a program’s data flow.
This thesis proposes a novel data dependency graph (DDG) analysis as an addi-
tion to angr’s analyses suite. This new analysis ingests a symbolic execution trace
in order to generate a directed acyclic graph of the program’s data dependencies. In
addition to the development of the backend logic needed to generate this graph, an
angr management view to visualize the DDG was implemented. This user interface
provides functionality for ancestor and descendant dependency tracing and sub-graph
creation. To evaluate the analysis, a user study was conducted to measure the view’s
efficacy in regards to binary analysis and software debugging. The study consisted
of a control group and experimental group attempting to solve a series of 3 chal-
lenges and subsequently providing feedback concerning perceived functionality and
comprehensibility pertaining to the view.
The results show that the view had a positive trend in relation to challenge-solving
accuracy in its target domain, as participants solved 32% more challenges 21% faster
when using the analysis than when using vanilla angr management.
ContributorsCapuano, Bailey Kellen (Author) / Shoshitaishvili, Yan (Thesis advisor) / Wang, Ruoyu (Thesis advisor) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2022
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
Visual applications – those that use camera frames as part of the application – provide a rich, context-aware experience. The continued development of mixed and augmented reality (MR/AR) computing environments furthers the richness of this experience by providing applications a continuous vision experience, where visual information continuously provides context for applications and the real world is augmented by the virtual. To understand user privacy concerns in continuous vision computing environments, this work studies three MR/AR applications (augmented markers, augmented faces, and text capture) to show that in a modern mobile system, the typical user is exposed to potential mass collection of sensitive information, posing privacy and security deficiencies to be addressed in future systems.
To address such deficiencies, a development framework is proposed that provides resource isolation between user information contained in camera frames and application access to the network. The design is implemented using existing system utilities as a proof of concept on the Android operating system and demonstrates its viability with a modern state-of-the-art augmented reality library and several augmented reality applications. Evaluation is conducted on the design on a Samsung Galaxy S8 phone by comparing the applications from the case study with modified versions which better protect user privacy. Early results show that the new design efficiently protects users against data collection in MR/AR applications with less than 0.7% performance overhead.
To address such deficiencies, a development framework is proposed that provides resource isolation between user information contained in camera frames and application access to the network. The design is implemented using existing system utilities as a proof of concept on the Android operating system and demonstrates its viability with a modern state-of-the-art augmented reality library and several augmented reality applications. Evaluation is conducted on the design on a Samsung Galaxy S8 phone by comparing the applications from the case study with modified versions which better protect user privacy. Early results show that the new design efficiently protects users against data collection in MR/AR applications with less than 0.7% performance overhead.
ContributorsJensen, Jk (Author) / LiKamWa, Robert (Thesis advisor) / Doupe, Adam (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2019