Efficient anomaly detection in tabular cybersecurity data using large language models

Efficient anomaly detection in tabular cybersecurity data using large language models

Abstract In cybersecurity, anomaly detection in tabular data is essential for ensuring information security. While traditional machine learning and deep learning methods have shown some success, they continue to face significant challenges in terms of generalization. To address these limitations, th...

Full description

Saved in:
Bibliographic Details
Main Authors: Xiaoyong Zhao, Xingxin Leng, Lei Wang, Ningning Wang, Yanqiong Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Anomaly detection
Large language models
Network security
Prompt engineering
Tabular data
Online Access:https://doi.org/10.1038/s41598-025-88050-z
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items