Zero Trust-NIDS: Extended Multi-View Approach for Network Trace Anonymization and Auto-Encoder CNN for Network Intrusion Detection

As the enterprise networks are being constantly targeted by sophisticated cyber threats, Zero Trust Security has been suggested to address existing threats. Zero Trust Security models have been recently proposed for outsourcing network security monitoring to third-party analysts. Therefore, the current trends of security monitoring needs to shift to "Never Trust, Always Verify". There are no concerns about analysis accuracy, if a zero trust model is resistant against security attacks. In this paper, a modified multi-view approach is proposed to preserve privacy in network traces, emphasizing the challenges needed to be tackled. We then extend the multi-view approach for the features that are not in the known list of the analyzer and extend the partitioning methods to a more balanced approach. In addition, in order to send any data to the analyzer, we propose to use an Auto-Encoder Convolutional Neural Network, which has the ability to receive any type of input attributes for detecting intrusive behavior. Our proposed multi-view approach outperforms existing works and improves efficiency by improving indistinguishability and preserving privacy for any attributes. The proposed Intrusion Detection System also outperforms existing works by up to 1% higher accuracy without any need for feature engineering.