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

Chunwang Wu, Xiaolei Liu, Kangyi Ding, Bangzhou Xin, Jiazhong Lu, Jiayong Liu, Cheng Huang
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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.
基于对比变异自动编码器和度量学习的 BCoT 攻击检测模型
随着区块链技术、云计算和物联网(IoT)的发展,区块链和物联网云(BCoT)已成为发展趋势。但安全问题已成为区块链与物联网(BCoT)发展的最大障碍。攻击检测模型是 BCoT 攻击揭示机制的重要组成部分。因此,攻击检测模型受到更多关注。由于针对物联网的网络攻击多种多样,传统的攻击检测模型并不适合物联网。本文提出了一种新的 BCoT 攻击检测模型,称为 cVAE-DML。该新型模型基于对比变异自动编码器(cVAE)和深度度量学习(DML)。通过训练 cVAE,该模型可生成攻击流量信息的私有特征以及攻击流量信息与正常流量信息之间的共享特征。根据这些生成的特征,建议的模型可以生成有代表性的新样本,以平衡训练数据集。最后,将 cVAE 的解码器连接到深度度量学习网络,以检测针对 BCoT 的攻击。使用 CIC-IDS 2017 数据集和 CSE-CIC-IDS 2018 数据集验证了 cVAE-DML 的效率。结果表明,即使在样本不平衡的情况下,cVAE-DML 也能提高攻击检测效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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