title: Analysing and scoring cybersecurity vulnerability reports using deep learning

authors: Zhang, Zijing

abstract: Vulnerability analysis plays a crucial role in cybersecurity, as it helps identify and mitigate potential threats to systems and networks. It is essential for organizations to understand the severity of vulnerabilities and prioritize their remediation efforts. This thesis focuses on the analysis and scoring of cybersecurity vulnerability reports using deep learning techniques. By leveraging large language models and machine learning algorithms, this thesis aims to improve the accuracy and efficiency of vulnerability classification and severity assessment.  

The research presented in this thesis includes the development of models to estimate the CVSS of vulnerabilities based on their descriptions, comparing the performance of AI models with human experts. For CVSS 3.1 severity level prediction task, the USE model achieves an accuracy of 0.73. The USE model outperforms human experts with a macro mean squared error of 0.128, which is four times lower than that of human experts (0.537). Meanwhile, quality of vulnerability descriptions is also analyzed to understand its impact on the performance of machine learning models. A BERT-based model (‘DesQ’) is proposed to assess the quality of vulnerability descriptions, achieving an accuracy of 0.76. A dataset of vulnerability descriptions with quality labels is curated and analyzed to understand the distribution of description quality across different vendors and reporting agencies. The analyzer BART model (‘EVAL’) identifies missing essential vulnerability aspects. Trained on a specialized quality dataset of 450 manually labeled entries, EVAL identifies Vulnerability Impact as the most frequently missing component across analyzed weaknesses. This thesis also explores the use of explainable AI techniques to provide insights into the decision-making process of machine learning models, enhancing their interpretability and trustworthiness in mapping root causes to vulnerabilities.

text: Analysing and scoring cybersecurity vulnerability reports using deep learning Vulnerability analysis plays a crucial role in cybersecurity, as it helps identify and mitigate potential threats to systems and networks. It is essential for organizations to understand the severity of vulnerabilities and prioritize their remediation efforts. This thesis focuses on the analysis and scoring of cybersecurity vulnerability reports using deep learning techniques. By leveraging large language models and machine learning algorithms, this thesis aims to improve the accuracy and efficiency of vulnerability classification and severity assessment.  

The research presented in this thesis includes the development of models to estimate the CVSS of vulnerabilities based on their descriptions, comparing the performance of AI models with human experts. For CVSS 3.1 severity level prediction task, the USE model achieves an accuracy of 0.73. The USE model outperforms human experts with a macro mean squared error of 0.128, which is four times lower than that of human experts (0.537). Meanwhile, quality of vulnerability descriptions is also analyzed to understand its impact on the performance of machine learning models. A BERT-based model (‘DesQ’) is proposed to assess the quality of vulnerability descriptions, achieving an accuracy of 0.76. A dataset of vulnerability descriptions with quality labels is curated and analyzed to understand the distribution of description quality across different vendors and reporting agencies. The analyzer BART model (‘EVAL’) identifies missing essential vulnerability aspects. Trained on a specialized quality dataset of 450 manually labeled entries, EVAL identifies Vulnerability Impact as the most frequently missing component across analyzed weaknesses. This thesis also explores the use of explainable AI techniques to provide insights into the decision-making process of machine learning models, enhancing their interpretability and trustworthiness in mapping root causes to vulnerabilities.

year: 2026

title: A manganese carbonyl complex derived from the P,N-bonding ligand (2-aminophenyl)diphenylphosphine

authors: Fester, Victor D.

subjects: manganese carbonyl

abstract: Thermal reaction of [H₃Mn₃(CO)₁₂] with Ph₂PC₆H₄NH₂-2 gives good yields of [Mn₂(CO)₆(PPh₂C₆H₄NH)₂], an X-ray single crystal structure determination of which reveals a P,N-chelating ligand with each of the NH groups, formed by deprotonation of the amine groups, bridging the two metal atoms.

text: A manganese carbonyl complex derived from the P,N-bonding ligand (2-aminophenyl)diphenylphosphine Thermal reaction of [H₃Mn₃(CO)₁₂] with Ph₂PC₆H₄NH₂-2 gives good yields of [Mn₂(CO)₆(PPh₂C₆H₄NH)₂], an X-ray single crystal structure determination of which reveals a P,N-chelating ligand with each of the NH groups, formed by deprotonation of the amine groups, bridging the two metal atoms.

year: 2007-01-01

title: "Everything has shaped you": Contributors to young Māori university students' ability to flourish

authors: Major, Darcie Ngahuia

subjects: Maori

abstract: There are many disparities when considering the health and wellbeing of Māori in Aotearoa today, and the education sector is no different, with Māori less likely to leave school with level 2 certificate or higher compared with non-Māori.  In response to the deficit orientated research on Māori and education, my thesis explores those factors contributing to young Māori tertiary students flourishing. My research includes five students from the University of Waikato who were between the ages of 17-24 years. A multi-method qualitative approach was utilised while engaging a Kaupapa Māori research framework. Participants were found to have a range of interconnected factors which contribute to their ability to flourish both inside and outside of the university.  Of particular importance to my participants was relational connectedness or whanaungatanga within and outside of the tertiary environment, landscapes which offered multiple forms of care, and notions of both collective and individual motivation. Some of the challenges experienced by my participants included a sense of socio-cultural isolation, the minimal inclusion of Māori worldviews and perspectives within the general curriculum, and the personal stress of responding to negative stereotypes of Maori as generally unproductive members of society. Flourishing for my participants is therefore multi-layered, and recognises the interconnected nature of health and wellbeing.

text: "Everything has shaped you": Contributors to young Māori university students' ability to flourish There are many disparities when considering the health and wellbeing of Māori in Aotearoa today, and the education sector is no different, with Māori less likely to leave school with level 2 certificate or higher compared with non-Māori.  In response to the deficit orientated research on Māori and education, my thesis explores those factors contributing to young Māori tertiary students flourishing. My research includes five students from the University of Waikato who were between the ages of 17-24 years. A multi-method qualitative approach was utilised while engaging a Kaupapa Māori research framework. Participants were found to have a range of interconnected factors which contribute to their ability to flourish both inside and outside of the university.  Of particular importance to my participants was relational connectedness or whanaungatanga within and outside of the tertiary environment, landscapes which offered multiple forms of care, and notions of both collective and individual motivation. Some of the challenges experienced by my participants included a sense of socio-cultural isolation, the minimal inclusion of Māori worldviews and perspectives within the general curriculum, and the personal stress of responding to negative stereotypes of Maori as generally unproductive members of society. Flourishing for my participants is therefore multi-layered, and recognises the interconnected nature of health and wellbeing.

year: 2017