ZT-NIDS: Zero Trust, Network Intrusion Detection System

Abeer Z. Alalmaie, Priyadarsi Nanda, Xiangjian He

Abstract: Zero Trust security can tackle various cyberthreats. Current trends in security monitoring must shift to a “never trust, always verify” approach, as data security is threatened when cloud-based third parties access network traces. Network Intrusion Detection System (NIDS) can be exploited to detect anomalous behaviour. Convolution Neural Network (CNN), Bi-directional Long Short Term Memory (BiLSTM) based classifiers and Auto-Encoder (AE) feature extractors have presented promising results in NIDS. AE feature extractor can compress the important information and train the unsupervised model. CNNs detect local spatial relationships, while BiLSTMs can exploit temporal interactions. Furthermore, Attention modules can capture content-based global interactions and can be applied on CNNs to attend to the significant contextual information. In this paper, we utilized the advantages of all AE, CNN and BiLSTM structures using a multi-head Self Attention mechanism to integrate CNN features for f