Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Using software bill of materials for software supply chain security and its generation impact on vulnerability detection(Montana State University - Bozeman, College of Engineering, 2024) O'Donoghue, Eric Jeffery; Chairperson, Graduate Committee: Clemente Izurieta; This is a manuscript style paper that includes co-authored chapters.Cybersecurity attacks threaten the lives and safety of individuals around the world. Improving defense mechanisms across all vulnerable surfaces is essential. Among surfaces, the software supply chain (SSC) stands out as particularly vulnerable to cyber threats. This thesis investigates how Software Bill of Materials (SBOM) can be utilized to assess and improve the security of software supply chains. An informal literature review reveals the paucity of studies utilizing SBOM to assess SSC security, which further motivates this research. Our research adopts the Goal/Question/Metric paradigm with two goals: firstly, to utilize SBOM technology to assess SSC security; secondly, to examine the impact of SBOM generation on vulnerability detection. The study unfolds in two phases. Initially, we introduce a novel approach to assess SSC security risks using SBOM technology. Utilizing analysis tools Trivy and Grype, we identify vulnerabilities across a corpus of 1,151 SBOMs. The second phase investigates how SBOM generation affects vulnerability detection. We analyzed four SBOM corpora derived from 2,313 Docker images by varying the SBOM generation tools (Syft and Trivy) and formats (CycloneDX 1.5 and SPDX 2.3). Using SBOM analysis tools (Trivy, Grype, CVE-bin-tool), we investigated how the vulnerability findings for the same software artifact changed according to the SBOM generation tool and format. The first phase demonstrates SBOMs use in identifying SSC vulnerabilities, showcasing their utility in enhancing security postures. The subsequent analysis reveals significant discrepancies in vulnerability detection outcomes, influenced by SBOM generation tools and formats. These variations underscore the necessity for rigorous validation and enhancement of SBOM technologies to secure SSCs effectively. This thesis demonstrates the use of SBOMs in assessing the security of SSCs. We underscore the need for stringent standards and rigorous validation mechanisms to ensure the accuracy and reliability of SBOM data. We reveal how SBOM generation affects vulnerability detection, offering insights that enhanced SBOM methodologies can help improve security. While SBOM is promising for enhancing SSC security, it is clear the SBOM space is immature. Extensive development, validation, and verification of analysis tools, generation tools, and formats are required to improve the usefulness of SBOMs for SSC security.Item Data-driven approaches for distribution grid modernization: exploring state estimaion, pseudo-measurement generation and false data detection(Montana State University - Bozeman, College of Engineering, 2023) Radhoush, Sepideh; Chairperson, Graduate Committee: Brad WhitakerDistribution networks must be regularly updated to enhance their performance and meet customer electricity requirements. Advanced technologies and infrastructure--including two- way communication, smart measuring devices, distributed generations in various forms, electric vehicles, variable loads, etc.--have been added to improve the overall efficiency of distribution networks. Corresponding to these new features and structures, the continuous control and monitoring of distribution networks should be intensified to keep track of any modifications to the distribution network performance. Distribution system state estimation has been introduced for real-time monitoring of distribution networks. State estimation calculations are highly dependent on measurement data which are collected from measurement devices in distribution networks. However, the installation of measurement devices is not possible at all buses to ensure the distribution network is fully observable. To address the lack of real measurements, pseudo- measurements are produced from historical load and generation data. Available measurements, along with physical distribution network topology, are fed into a state estimation algorithm to determine system state variables. Then, state estimation results are sent to a control center for further processing to enhance distribution network operation. However, the accuracy of state estimation results could be degraded by false data injection attacks on measurement data. If these attacks are not detected, distribution network operation could be significantly influenced. Different methods have been developed to enhance a distribution network operation and management. Machine learning approaches have also been identified to be beneficial in solving different types of problems in a power grid. In this dissertation, machine learning is applied to three areas of distribution systems: generating pseudo-measurements, performing distribution system state estimation calculations, and detecting false data injection attacks on measurement data. In addition to addressing these areas individually, machine learning is used to simultaneously perform distribution system state estimation calculation and false data injection attack detection. This is done by taking advantage of conventional and smart measurement data at different time scales. The results reveal that the operation and performance of a distribution network are improved using machine learning algorithms, leading to more effective power grid modernization.Item An evaluation of graph representation of programs for malware detection and categorization using graph-based machine learning methods(Montana State University - Bozeman, College of Engineering, 2023) Pearsall, Reese Andersen; Chairperson, Graduate Committee: Clemente IzurietaWith both new and reused malware being used in cyberattacks everyday, there is a dire need for the ability to detect and categorize malware before damage can be done. Previous research has shown that graph-based machine learning algorithms can learn on graph representations of programs, such as a control flow graph, to better distinguish between malicious and benign programs, and detect malware. With many types of graph representations of programs, there has not been a comparison between these different graphs to see if one performs better than the rest. This thesis provides a comparison between different graph representations of programs for both malware detection and categorization using graph-based machine learning methods. Four different graphs are evaluated: control flow graph generated via disassembly, control flow graph generated via symbolic execution, function call graph, and data dependency graph. This thesis also describes a pipeline for creating a classifier for malware detection and categorization. Graphs are generated using the binary analysis tool angr, and their embeddings are calculated using the Graph2Vec graph embedding algorithm. The embeddings are plotted and clustered using K-means. A classifier is then built by assigning labels to clusters and the points within each cluster. We collected 2500 malicious executables and 2500 benign executables, and each of the four graph types is generated for each executable. Each is plugged into their own individual pipeline. A classifier for each of the four graph types is built, and classification metrics (e.g. F1 score) are calculated. The results show that control flow graphs generated from symbolic execution had the highest F1 score of the four different graph representations. Using the control flow graph generated from symbolic execution pipeline, the classifier was able to most accurately categorize trojan malware.Item A framework to assess bug-bounty platforms based on potential attack vectors(Montana State University - Bozeman, College of Engineering, 2022) McCartney, Susan Ann; Co-chairs, Graduate Committee: Clemente Izurieta and Mike WittieCorporate computer security is becoming increasingly important because the frequency and severity of cyberattacks on businesses is high and increasing. One way to improve the security of company software is for a company to hire a third party to identify and report vulnerabilities, blocks of code that can be exploited. A bug-bounty program incentivizes ethical hackers (herein, 'researchers') to find and fix vulnerabilities before they can be exploited. For this reason, bug-bounty programs have been increasing in popularity since their inception a decade ago. However, the increase in their use and popularity also increases the likelihood of the companies being targeted by malicious actors by using a bug-bounty programs as the medium. The literature review and investigation into the rules and requirements for bug-bounty platform revealed that though the bug-bounty programs can improve a vendor's security, the programs still contain a serious security flaw. The platforms are not required to scan reports for malware and there is no guidance requesting the vendors scan for malware. This means it is possible to perform a cyberattack using malware as a report attachment. Through data collection from 22 platforms, an observational case study, and analysis of different malware, I have created a tool to assist vendors in selecting the platform of best fit and characterize the possible attack surfaces presented from the file options allowed on the platform. The outcome from this research is evidence of the importance of understanding the malware files used as report attachments. However, more research is needed in the relationship between file extensions and malware in order to thoroughly comprehend the attack surface capabilities, and to understand the trade-offs between security and convenience.Item Analyzing the security of C# source code using a hierarchical quality model(Montana State University - Bozeman, College of Engineering, 2022) Harrison, Payton Rae; Chairperson, Graduate Committee: Clemente IzurietaIn software engineering, both in government and in industry, there are no universal standards or guidelines for security or quality. There is an increased need for evaluating the security of source code projects, which is made apparent by the number of real-world cyber attacks that have taken place recently. Our research goal is to design and develop a security quality model that helps stakeholders assess the security of C# source code projects. While there are many analysis tools that can be used to identity security vulnerabilities, the use of a model is beneficial in integrating multiple analysis tools to have better coverage over the number of security vulnerabilities detected (compared to the use of a single tool) and to aggregate these vulnerabilities upward into a broader security quality context. We accomplished our goal by developing and validating a hierarchical security quality model (PIQUE-C#-Sec) to evaluate the security quality of software written in C#. This model is an operationalized model using PIQUE, or the Platform for Investigative software Quality Understanding and Evaluation. PIQUE-C#-Sec improves upon previous security quality models and quality models that precede it by focusing on being specific, flexible, and extensible. This thesis introduces the model design for PIQUE-C#-Sec and examines the results from the efforts of validating the PIQUE-C#-Sec model. This model was validated using sensitivity analysis, which consisted of collecting data on benchmark repositories and observing if and how the PIQUE-C#-Sec model output varied as a function of these repository attributes. Additionally, the model was analyzed by testing to see how the PIQUE-C#-Sec model node values changed because of the tools reporting additional vulnerabilities. Based on these results, we conclude that the PIQUE-C#-Sec model is effective for stakeholders to use when evaluating C# source code, and the model can be used as a security quality gate for evaluating these projects.Item The analysis of binary file security using a hierarchical quality model(Montana State University - Bozeman, College of Engineering, 2022) Johnson, Andrew Lucas; Chairperson, Graduate Committee: Clemente IzurietaSoftware security is commanding significant attention from practitioners. In many organizations, security assessment has been integrated into the software development lifecycle, which allows for continuous monitoring of software weaknesses and vulnerabilities throughout the development process. One often overlooked aspect of the software development lifecycle is the end of the lifecycle. Prior to delivering software to customers, many vendors digitally sign and compile source code into a binary. In binary form, analysis may be done to reveal security flaws that were not present in the original code or that were injected at some point between the code being written and the code being compiled. Our research goal is to improve our ability to assess the security quality of a binary from different stakeholders' perspectives. While many analysis tools exist that identify security flaws, there is little work done to enable the use of multiple tools, which is necessary to identify different types of security flaws. To accomplish our goal, we approach the problem from the perspective of quality modeling. We have designed and developed a software quality model for assessing security quality in binaries (PIQUE-Bin) and operationalized the model by using PIQUE, the Platform for Investigative software Quality Understanding and Evaluation. The design of our model is based on the Microsoft STRIDE model and the software development view of the Common Weakness Enumeration (CWE). The model produces a relative and subjective security score for a binary file. An informal literature review reveals a lack of model-based security metrics targeting binary files, which helped motivate this research. To enhance the validity of this work, a sensitivity analysis assessment based on a benchmark repository of 700 binary files was performed. Model output is validated by measuring tool output sensitivity and calibrated against the presence of injected vulnerabilities. We find that our model is able to measure the security quality of binaries relative to the benchmark repository.