广东工业大学学报2026，Vol.43Issue(2)：30-40,11.DOI:10.12052/gdutxb.250011
多尺度对比嵌入增强的自适应入侵检测模型
Multi-Scale Contrastive Embedding Enhanced Adaptive Intrusion Detection Model
摘要
Abstract
In intrusion detection,while certain unsupervised models can detect unknown attack types using thresholds,they usually fall to effectively utilize the identified traffic patterns to capture the similarities and differences across traffic flows.As a result,unknown attack traffics are frequently misclassified as normal traffic.To address this issue,this paper proposes a Multi-scale Contrastive Embedding Enhanced Adaptive Intrusion Detection Model(MCE-IDM).It employs hierarchical contrastive learning to integrate known attack types with their associated data features,generating embeddings that are subsequently combined with the original data to train an unsupervised model.Furthermore,a lightweight gradient boosting machine is used for feature selection,which significantly reduces the time complexity during this phase compared to previous models.Experimental results on multiple datasets demonstrate that the proposed model not only exhibits stable performance but also improves the Matthews Correlation Coefficient(MCC)by 15.78 percentage points over baseline models on a highly imbalanced subset of data.The proposed method also consistently achieves competitive results across other subsets.关键词
入侵检测/无监督模型/特征降维/对比学习Key words
intrusion detection/unsupervised model/feature dimensionality reduction/contrastive learning分类
信息技术与安全科学引用本文复制引用
王桂鑫,吴晓鸰,冯永晋,Hoon Heo..多尺度对比嵌入增强的自适应入侵检测模型[J].广东工业大学学报,2026,43(2):30-40,11.基金项目
广东省重点领域研发计划项目(2019B010139002) （2019B010139002）
广东省国际科技合作领域资助项目(2019A050513010) （2019A050513010）
