Titlebook: Data and Applications Security and Privacy XXXVIII; 38th Annual IFIP WG Anna Lisa Ferrara,Ram Krishnan Conference proceedings 2024 IFIP In

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DT-Anon: Decision Tree Target-Driven Anonymizationarn from the anonymized data. The basic idea of our approach is to perform the anonymization process on partitions produced by a decision tree driven by the target of the classification task. Each partition is then independently anonymized, to limit the impact of anonymization on the attributes and

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ALERT: A Framework for?Efficient Extraction of?Attack Techniques from?Cyber Threat Intelligence Repoel cybersecurity framework which leverages active learning strategies in conjunction with an LLM. This approach dynamically selects the most informative instances for annotation, thereby achieving comparable performance with a significantly smaller dataset. By prioritizing the annotation of samples

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