Document detail
ID
oai:arXiv.org:2409.15072
Topic
Computer Science - Cryptography an... Computer Science - Computation and... Computer Science - Human-Computer ... Computer Science - Machine Learnin...Author
Srikanth, Sanchana Hasanuzzaman, Mohammad Meem, Farah TasnurCategory
Computer Science
Year
2024
listing date
9/25/2024
Keywords
llms intelligenceAbstract
Large Language Models (LLMs) have the potential to significantly enhance threat intelligence by automating the collection, preprocessing, and analysis of threat data.
However, the usability of these tools is critical to ensure their effective adoption by security professionals.
Despite the advanced capabilities of LLMs, concerns about their reliability, accuracy, and potential for generating inaccurate information persist.
This study conducts a comprehensive usability evaluation of five LLMs ChatGPT, Gemini, Cohere, Copilot, and Meta AI focusing on their user interface design, error handling, learning curve, performance, and integration with existing tools in threat intelligence enrichment.
Utilizing a heuristic walkthrough and a user study methodology, we identify key usability issues and offer actionable recommendations for improvement.
Our findings aim to bridge the gap between LLM functionality and user experience, thereby promoting more efficient and accurate threat intelligence practices by ensuring these tools are user-friendly and reliable.
Srikanth, Sanchana,Hasanuzzaman, Mohammad,Meem, Farah Tasnur, 2024, Evaluating the Usability of LLMs in Threat Intelligence Enrichment
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