PUNet:A Semi-Supervised Anomaly Detection Model for Network Anomaly Detection Based on Positive Unlabeled Data  

作　　者：Gang Long Zhaoxin Zhang 

机构地区：[1]Faculty of Computing,Harbin Institute of Technology,Harbin,150000,China

出　　处：《Computers, Materials & Continua》2024年第10期327-343,共17页计算机、材料和连续体（英文）

摘　　要：Network anomaly detection plays a vital role in safeguarding network security.However,the existing network anomaly detection task is typically based on the one-class zero-positive scenario.This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set.This phenomenon is known as prediction drift.Additionally,the rarity of anomaly data,often masked by normal data,further complicates network anomaly detection.To address these challenges,we propose the PUNet network,which ingeniously combines the strengths of traditional machine learning and deep learning techniques for anomaly detection.Specifically,PUNet employs a reconstruction-based autoencoder to pre-train normal data,enabling the network to capture potential features and correlations within the data.Subsequently,PUNet integrates a sampling algorithm to construct a pseudo-label candidate set among the outliers based on the reconstruction loss of the samples.This approach effectively mitigates the prediction drift problem by incorporating abnormal samples.Furthermore,PUNet utilizes the CatBoost classifier for anomaly detection to tackle potential data imbalance issues within the candidate set.Extensive experimental evaluations demonstrate that PUNet effectively resolves the prediction drift and data imbalance problems,significantly outperforming competing methods.

关 键 词：Network anomaly detection representation learning candidate set CatBoost 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]