Coastal zones are dynamic and vulnerable regions, demanding accurate, scalable monitoring tools to inform environmental management and hazard mitigation. While satellite imagery and CNN-based classifiers have improved automated mapping, their reliance on unstructured pixel data limits contextual understanding. This study presents the first fine-tuning of a multi-modal large language model (MLLM), Qwen2.5, on 12-channel satellite input for multilabel coastal classification, demonstrating how architectural adaptation enables integration of spectral, topographic, and derived features beyond RGB. We compare this approach to a ResNet-50 baseline and state-of-the-art prompting methods using GPT-4o and LLaMA-3.2. Our experiments on the CoastBench dataset reveal that MLLMs benefit substantially from few-shot prompting with diverse, balanced sampling and that fine-tuning Qwen2.5 with full 12-channel input outperforms its RGB-only variant. An ablation study quantifies the importance of elevation and water-sensitive indices, while a human benchmark exposes a performance ceiling near F1 ≈ 0.70 due to label ambiguity. Our findings suggest that while MLLMs can rival traditional models and offer interpretability benefits, future gains depend on dataset quality, input diversity, and prompting strategy design.

MalPaCA makes use of unsupervised machine learning to provide malware capability assessment by clustering the temporal behaviour of malware network packet traces. A comparative analysis was performed on various clustering algorithms to determine the best clustering algorithm in terms of network behaviour discovery. The clustering algorithms included in the analysis were HDBSCAN, OPTICS, Agglomerative Hierarchical Clustering and K-medoids. Metrics that capture cluster separation, cohesion, purity and completeness were used to determine the performance of the clustering algorithms. Agglomerative Hierarchical Clustering had the lowest total error of 0.950 in the comparative analysis compared to the baseline HDBScan with an error of 1.381.