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Summary of Uncovering Semantics and Topics Utilized by Threat Actors to Deliver Malicious Attachments and Urls, By Andrey Yakymovych et al.


Uncovering Semantics and Topics Utilized by Threat Actors to Deliver Malicious Attachments and URLs

by Andrey Yakymovych, Abhishek Singh

First submitted to arxiv on: 11 Jul 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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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 proposes an unsupervised topic modeling approach using BERTopic to identify semantic cues in emails that contain malicious attachments and URLs. By preprocessing emails and employing multilingual embedding models like BGE-M3, the authors cluster emails by semantic similarity using algorithms like HDBSCAN and OPTICS. The study also utilizes Phi3-Mini-4K-Instruct for semantic analysis and hLDA for thematic understanding of threat actor patterns. The paper’s main contribution is the evaluation and comparison of different clustering algorithms on topic quantity, coherence, and diversity metrics, providing insights into the semantics and topics used by threat actors to deliver malicious content.
Low GrooveSquid.com (original content) Low Difficulty Summary
The study aims to improve email-based malware detection by using a new approach that focuses on semantic cues in emails. Instead of just looking at the attachment or URL itself, this method looks at the words and phrases used in the email to see if they give away whether it’s malicious or not. The researchers use special computer programs called topic models to group similar emails together based on their content. This helps them understand what kind of language and themes are commonly used by hackers when sending malware through emails.

Keywords

* Artificial intelligence  * Clustering  * Embedding  * Semantics  * Unsupervised