西华大学学报（自然科学版）2025，Vol.44Issue(5)：39-47,69,10.DOI:10.12198/j.issn.1673-159X.5535
基于分组序列图像表征和视觉Transformer模型的网络入侵检测系统
The Network-Based Intrusion Detection System Based on Packet Sequence Image Representation and the Vision Transformer Model
摘要
Abstract
With the continuous emergence of new network attacks,network-based intrusion detection systems(NIDS)have become an indispensable protection mechanism in network security.To enhance the accuracy and real-time performance of intrusion detection,a NIDS based on packet sequence representa-tion and deep learning model is proposed.Firstly,packet headers and payload data were analyzed using a packet parsing algorithm to effectively extract packet sequence features.Subsequently,an image construc-tion algorithm was employed to encode the temporal relationships within the feature set of packets,creating RGB images for the forward and backward features of the same flow.Finally,an intrusion detection model based on ViT was developed to perform intrusion detection based on image classification results,and the layered focal loss function was employed within the ViT model to improve classification performance and address data imbalance issues.Experimental results on public NIDS datasets demonstrate that the proposed system significantly enhances intrusion detection performance compared to existing NIDS,achieving a high detection rate of 98%(up to 100%).Given the increasing complexity and diversity of current network intru-sions,the proposed method will contribute to improved network security.关键词
网络安全/入侵检测系统/深度学习/分组解析/图像表征/视觉Transformer/时序关系Key words
network security/intrusion detection system/deep learning/packet parsing/image representation/vision transformer/temporal relationship分类
信息技术与安全科学引用本文复制引用
丁永红,王晓勇..基于分组序列图像表征和视觉Transformer模型的网络入侵检测系统[J].西华大学学报（自然科学版）,2025,44(5):39-47,69,10.基金项目
安徽省高等学校省级质量工程项目(2021cjrh044). （2021cjrh044）
