Summary of Towards Characterizing Cyber Networks with Large Language Models, by Alaric Hartsock et al.
Towards Characterizing Cyber Networks with Large Language Models
by Alaric Hartsock, Luiz Manella Pereira, Glenn Fink
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 This paper presents a novel approach to threat hunting, which involves analyzing large volumes of noisy and high-dimensional data to identify sparse adversarial behavior. The authors propose the Cyber Log Embeddings Model (CLEM), a prototype tool that leverages latent features of cyber data to detect anomalies. CLEM is trained on Zeek network traffic logs from both real-world production networks and Internet of Things (IoT) cybersecurity testbeds, and is deliberately over-trained on sliding windows of data to characterize each window closely. The authors evaluate the effectiveness of their approach using the Adjusted Rand Index (ARI), comparing k-means clustering of CLEM output to expert labeling of the embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that we can use special techniques from language processing to better understand and identify security threats in computer networks. The researchers developed a tool called CLEM, which takes large amounts of network data as input and produces results that are easy to analyze. They tested CLEM on real-world data and found it was good at identifying unusual patterns that might indicate malicious activity. |
Keywords
» Artificial intelligence » Clustering » K means
