Summary of Command-line Risk Classification Using Transformer-based Neural Architectures, by Paolo Notaro et al.
Command-line Risk Classification using Transformer-based Neural Architectures
by Paolo Notaro, Soroush Haeri, Jorge Cardoso, Michael Gerndt
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A novel transformer-based command risk classification system is proposed to monitor Operations and Maintenance (O&M) activities in cloud computing environments. The system leverages Large Language Models (LLM) to accurately classify CLI commands, accounting for the language characteristics of scripting languages like Bash or PowerShell, and rare dangerous commands. This approach exploits transfer learning to provide accurate classification. Evaluation on a realistic dataset of production commands demonstrates the effectiveness of the proposed model. Furthermore, the system can be applied to other security-related tasks, such as dangerous command interception and auditing of existing rule-based systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to keep cloud computing safe is being developed. Right now, large computer networks are vulnerable to damage because people aren’t careful enough when typing commands. To fix this problem, a team of researchers created a special system that can predict which commands might cause trouble and block them before they do any harm. This system uses powerful machines called Large Language Models to understand the types of commands used in scripts like Bash or PowerShell. It’s really good at catching rare mistakes too! The scientists tested their idea on real data from cloud computing and showed how it can be used for other security tasks as well. |
Keywords
» Artificial intelligence » Classification » Transfer learning » Transformer
