@inproceedings{f9ea2877bfe1407aa1e4f05e04cf7bde,
title = "Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums",
abstract = "With recent trends indicating cyber crimes increasing in both frequency and cost, it is imperative to develop new methods that leverage data-rich hacker forums to assist in combating ever evolving cyber threats. Defining interactions within these forums is critical as it facilitates identifying highly skilled users, which can improve prediction of novel threats and future cyber attacks. We propose a method called Next Paragraph Prediction with Instructional Prompting (NPP-IP) to predict thread structures while grounded on the context around posts. This is the first time to apply an instructional prompting approach to the cybersecurity domain. We evaluate our NPP-IP with the Reddit dataset and Hacker Forums dataset that has posts and thread structures of real hacker forums{\textquoteright} threads, and compare our method{\textquoteright}s performance with existing methods. The experimental evaluation shows that our proposed method can predict the thread structure on average 14 % better than existing best methods allowing for better social network prediction based on forum interactions.",
keywords = "Cybersecurity, Instructional prompts, Next paragraph prediction, Social network, Thread structure, Thread structure prediction, Unstructured forums",
author = "Kazuaki Kashihara and Pal, {Kuntal Kumar} and Chitta Baral and Trevino, {Robert P.}",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Intelligent Systems Conference, IntelliSys 2023 ; Conference date: 07-09-2023 Through 08-09-2023",
year = "2024",
doi = "10.1007/978-3-031-47715-7_51",
language = "English (US)",
isbn = "9783031477140",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "766--781",
editor = "Kohei Arai",
booktitle = "Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 3",
address = "Germany",
}