The article discusses the integration of large language models (LLMs) into cybersecurity, specifically focusing on their ability to automate tasks related to malware analysis. Researchers have evaluated four LLMs on real-world scripts from the Emotet malware campaign, finding that these models can effectively deobfuscate malicious payloads, despite not achieving full accuracy. The study suggests a promising future for integrating LLMs into threat intelligence frameworks, emphasizing the necessity of fine-tuning these models for better performance in identifying obfuscated malware.
The integration of large language models (LLMs) into cybersecurity enhances automation, enabling the processing of diverse data to identify anomalies and fraud.
Our research shows that state-of-the-art LLMs are capable of deobfuscating malicious scripts associated with Emotet malware, though accuracy remains an area of improvement.
Collection
[
|
...
]