Attack detection model for BCoT based on contrastive variational autoencoder and metric learning

• Published in: Journal of Cloud Computing
• Main Authors: Chunwang Wu, Xiaolei Liu, Kangyi Ding, Bangzhou Xin, Jiazhong Lu, Jiayong Liu, Cheng Huang
• Format: Article in Journal/Newspaper
• Language: English
• Published: SpringerOpen 2024
• Subjects: Attack detection; BCoT; Metric learning; Contrastive variational autoencoder; Oversample; Computer engineering. Computer hardware; TK7885-7895; Electronic computers. Computer science; QA75.5-76.95; DML
• Online Access: https://doi.org/10.1186/s13677-024-00678-w; https://doaj.org/article/a3738031ff1a423bbfeaeabe72621d1b

Abstract With development of blockchain technology, clouding computing and Internet of Things (IoT), blockchain and cloud of things (BCoT) has become development tendency. But the security has become the most development hinder of BCoT. Attack detection model is a crucial part of attack revelation mechanism for BCoT. As a consequence, attack detection model has received more concerned. Due to the great diversity and variation of network attacks aiming to BCoT, tradition attack detection models are not suitable for BCoT. In this paper, we propose a novel attack detection model for BCoT, denoted as cVAE-DML. The novel model is based on contrastive variational autoencoder (cVAE) and deep metric learning (DML). By training the cVAE, the proposed model generates private features for attack traffic information as well as shared features between attack traffic information and normal traffic information. Based on those generated features, the proposed model can generate representative new samples to balance the training dataset. At last, the decoder of cVAE is connected to the deep metric learning network to detect attack aiming to BCoT. The efficiency of cVAE-DML is verified using the CIC-IDS 2017 dataset and CSE-CIC-IDS 2018 dataset. The results show that cVAE-DML can improve attack detection efficiency even under the condition of unbalanced samples.