使用联合学习和改进变压器的信息系统网络入侵检测方法

IF 4.1 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal on Semantic Web and Information Systems Pub Date : 2023-12-15 DOI:10.4018/ijswis.334845
Qi Zhou, Zhoupu Wang
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引用次数: 0

摘要

针对现有大多数入侵检测方法在分析海量数据时存在的检测时间长、安全性和准确性低等问题,提出了一种利用联合学习和改进的变换器的信息系统网络入侵检测方法。首先,构建了基于联盟学习框架的网络入侵检测系统,并将变换器模型作为其通用检测模型。然后,对数据集进行划分,利用改进的生成对抗网络进行数据增强,生成新的样本集,以克服少数类样本的影响。同时,将新样本输入变换器局部模型,进行网络攻击类型检测和分析。最后,作者汇总了每个局部模型的检测结果,并将其输入 Softmax 分类器,得到最终的分类预测结果。
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A Network Intrusion Detection Method for Information Systems Using Federated Learning and Improved Transformer
A network intrusion detection method for information systems using federated learning and improved transformer is proposed to address the problems of long detection time and low security and accuracy when analyzing massive data in most existing intrusion detection methods. Firstly, a network intrusion detection system is constructed based on a federated learning framework, and the transformer model is used as its universal detection model. Then, the dataset is divided and an improved generative adversarial network is used for data augmentation to generate a new sample set to overcome the influence of minority class samples. At the same time, the new samples are input into the transformer local model for network attack type detection and analysis. Finally, the authors aggregate the detection results of each local model and input them into the Softmax classifier to obtain the final classification prediction results.
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来源期刊
CiteScore
6.20
自引率
12.50%
发文量
51
审稿时长
20 months
期刊介绍: The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.
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