Work

Research on network security threat detection system based on large language model Optimized by GloVe technology

Year: 2025

Type: conference-paper

Abstract: With the increasing complexity of network attacks, traditional security detection methods have limitations in dealing with new threats and zero-day attacks. In this paper, we propose a large language model network security threat detection system based on BERT. The system takes the network traffic log as the data source, extracts the state features and constructs the feature map vector. At the same time, it combines GloVe word embedding to optimize the feature expression to improve the semantic understanding ability of data. Experimental results show that the proposed method is superior to traditional methods in abnormal traffic detection and unknown attack recognition, and has high accuracy and generalization ability, which provides an effective solution for intelligent network security defense.

Source: International Conference on Computer Network Security and Software Engineering (CNSSE)

DOI: 10.1145/3732365.3732430

Authors: Yingke Xiao, Huiyong Liu, Bilali Aximu, Zirui Shao, Yufei Feng

Institutions: Beijing Information Science & Technology University, China Telecom

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411672641

FWCI:

Cites: 6

Cited by:

Related to: 10