Distilling Contextual Embeddings Into A Static Word Embedding For Improving Hacker Forum Analytics
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This paper introduces Hack2Vec, a specialized way of converting hacker-forum text into numerical representations that machine-learning models can use. It transfers knowledge from the powerful BERT language model into a lightweight, forum-specific word embedding tailored to hacker language. On a standard forum classification task, Hack2Vec outperforms other widely used embedding methods across accuracy and related metrics.
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Benjamin M. Ampel
Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)
My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.