Summary of Psychological Profiling in Cybersecurity: a Look at Llms and Psycholinguistic Features, by Jean Marie Tshimula et al.
Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features
by Jean Marie Tshimula, D’Jeff K. Nkashama, Jean Tshibangu Muabila, René Manassé Galekwa, Hugues Kanda, Maximilien V. Dialufuma, Mbuyi Mukendi Didier, Kalonji Kalala, Serge Mundele, Patience Kinshie Lenye, Tighana Wenge Basele, Aristarque Ilunga, Christian N. Mayemba, Nathanaël M. Kasoro, Selain K. Kasereka, Hardy Mikese, Pierre-Martin Tardif, Marc Frappier, Froduald Kabanza, Belkacem Chikhaoui, Shengrui Wang, Ali Mulenda Sumbu, Xavier Ndona, Raoul Kienge-Kienge Intudi
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of Large Language Models (LLMs) in cybersecurity by analyzing textual data for identifying psychological traits of threat actors. The authors explore how psycholinguistic features, such as linguistic patterns and emotional cues, can be incorporated into cybersecurity frameworks to improve defense mechanisms against evolving threats. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows that LLMs can be used to analyze textual data and identify the psychological traits of cyber threat actors. By incorporating psycholinguistic features like language patterns and emotional cues, cybersecurity practices can be improved to better defend against emerging threats. |
