Summary of Contextual Chart Generation For Cyber Deception, by David D. Nguyen et al.
Contextual Chart Generation for Cyber Deception
by David D. Nguyen, David Liebowitz, Surya Nepal, Salil S. Kanhere, Sharif Abuadbba
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| 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 presents a novel approach to creating realistic honeypot files that mimic sensitive documents, enhancing security measures against malicious actors. By leveraging large language models, the authors aim to reduce the time, cost, and effort required to generate high-quality honeyfiles with varied content, including charts, tables, images, and text. This development enables the creation of semantically consistent and realistic honeypots that can effectively deceive intruders, providing valuable insights into their intentions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates special files called “honeyfiles” that trick hackers into thinking they’ve found important documents on a computer that’s already been hacked. These honeyfiles are designed to look like real documents, but they’re actually fake and help security experts learn more about the hacker’s goals. The problem is that making these honeyfiles takes a lot of time and effort. New language tools can make it easier to create good honeyfiles, but the files also need charts, tables, and pictures that are believable and match what real documents look like. |
