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Research Commons is the University of Waikato's open access research repository, housing research publications and theses produced by the University's staff and students.

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Response-level identification of Cloud API misconfigurations using large language models
(Scilight Press, 2026) Zahid, Farzana; Krishna, Akshay
Application Programming Interfaces (APIs) are a set of rules that enable communication, data exchange, and automated interactions between applications and services. With the rapid advancement of cloud computing, APIs have evolved from simple data-access interfaces into critical components for managing, configuring, and orchestrating cloud resources. Most modern cloud platforms rely on RESTful APIs for provisioning cloud resources, applying configurations, and maintaining services. As a result, APIs misconfigurations have become a critical cloud security threat that can lead to sensitive data exposure, unauthorized access, or operational disruptions. Identifying these misconfigurations is challenging because traditional rule-based and static analysis methods often fail to capture complex, context-dependent configuration issues and system behaviors. In this study, we investigate the use of Large Language Models (LLMs) to detect security misconfigurations directly from cloud API response data. By treating API responses as representations of a system’s configuration state, we assess whether LLMs can effectively identify potential security risks. We evaluate five LLMs using a unified zero-shot prompting approach and compare their performance with and without Retrieval-Augmented Generation (RAG) to understand the impact of external knowledge on misconfiguration detection. The study not only focuses on each model’s ability to identify configuration components and detect misconfigurations, but also evaluates their capability to accurately determine the number of misconfigurations and generate clear, actionable security explanations. Our preliminary results show that Meta Llama Instruct combined with RAG achieves the reliable performance for identifying security misconfigurations in cloud API responses. This study provides new insights into the practicality of LLM-driven API cloud security analysis and paves the way for future research.
Most versus least: A meta-analysis of stimulus overselectivity
(The University of Waikato, 2026) Simoné; Edwards, Timothy L.
Stimulus overselectivity (OS) occurs when a person's behaviour is controlled by only a limited subset of available cues. This phenomenon is thought to contribute to learning and social challenges in individuals with autism spectrum disorder (ASD). This thesis presents a multilevel meta-analysis quantifying the magnitude of OS in individuals with ASD compared to typically developing (TD) peers. Analyses of 15 studies, producing 41 unique effects, showed a statistically significant population gap (p = .036). The average level of OS was 35.16% for ASD groups and 20.96% for TD groups. Both groups exhibited a general tendency for OS, but the effect was more pronounced in ASD. Notably, this difference was fragile: when the oldest study (Gersten, 1983) was excluded, the gap was no longer statistically significant (p = .129). A major limitation was that only 15 out of 158 relevant studies provided enough data for quantitative analysis, highlighting a widespread reporting gap. These findings were consistent with stimulus control hierarchies, suggesting that the systematic narrowing of control observed in individuals with ASD may be sensitive to non-linear diagnostic shifts within the evolving diagnostic landscape. For practitioners, the findings emphasise the importance of assessing which cues guide a learner’s responses and using targeted strategies to broaden cue control in educational settings. For researchers, these results highlight the need for consistent, standardised reporting of element-level data and variability to advance theoretical understanding of overselectivity across populations.
Indicators of fetal engagement with the environment
(The University of Waikato, 2026) Sheik-Ebrahim, Kousar; Reid, Vincent
Fetal behaviour provides a non-invasive window into early fetal development, reflecting the maturation of sensory and motor systems. This thesis investigates the developmental trajectory of spontaneous and stimulus-evoked behaviours across late gestation (33–37 weeks) to identify behavioural indicators of selective sensory engagement. Secondary analyses were conducted on high-resolution ultrasound recordings using a systematic behavioural measurement framework, incorporating standardised coding of fetal behavioural states (FBS), fetal body movements (FBM), and fetal eye movements (FEM) to enhance observational reliability and analytical rigor. Quantitative analysis, including Dynamic Time Warping (DTW), was employed to distinguish baseline behavioural activity from responses elicited by controlled auditory and visual stimuli. Study 1 (N = 57) investigated patterns of FBM and FEM to determine whether observed behaviours reflected spontaneous activity or environmentally driven engagement. Results indicated that from approximately 33 weeks’ gestation onward, FEM classifications increasingly aligned with behavioural markers of environmental engagement, suggesting progressive maturation of sensory–behavioural integration. Study 2 (N = 24) examined fetal behavioural responses to musical stimuli varying in timbre (string versus piano) and harmonic modality (major versus minor chords). We observed distinct patterns of structured FEM and FBM across auditory conditions, indicating selective behavioural modulation in response to specific acoustic properties. The findings from this cross-sectional study suggest that late-gestation fetuses demonstrate emerging capacities for selective engagement with environmental stimuli before birth. This research advances understanding of prenatal sensory and perceptual processing and supports refinement of fetal behavioural frameworks, with implications for clinical monitoring and foundational research into early perceptual learning.
Periodic boundary conditions and G2 cosmology
(IOP Publishing, 2023) Coley, Alan A.; Lim, Woei Chet
In the standard concordance cosmology the spatial curvature is assumed to be constant and zero (or at least very small). In particular, in numerical computations of the structure of the universe using N-body simulations, exact periodic boundary conditions are assumed which constrains the spatial curvature. In order to conﬁrm this qualitatively, we numerically evolve a special class of spatially inhomogeneous G2 models with both periodic initial data and non periodic initial data using zooming techniques. We consequently demonstrate that in these models periodic initial conditions do indeed suppress the growth of the spatial curvature as the models evolve away from their initial isotropic and spatially homogeneous state, thereby verifying that the spatial curvature is necessarily very small in standard cosmology.
Automatic detection of Android crypto ransomware using supervisor reduction
(Springer Nature, 2024) Chew, Christopher J.W.; Malik, Robi; Kumar, Vimal; Patros, Panos
This paper proposes a finite-state machine based approach to recognise crypto ransomware based on their behaviour. Malicious and benign Android applications are executed to capture the system calls they generate, which are then filtered and tokenised and converted to finite-state machines. The finite-state machines are simplified using supervisor reduction, which generalises the behavioural patterns and produces compact classification models. The classification models can be implemented in a lightweight monitoring system to detect malicious behaviour of running applications quickly. An extensive set of cross validation experiments is carried out to demonstrate the viability of the approach, which show that ransomware can be classified accurately with an F1 score of up to 93.8%.