HAIS-IDS: A hybrid artificial immune system model for intrusion detection in IoT

Abstract

The application of the Internet of Things (IoT) is increasing exponentially, the dynamic data flow and distributive operation over low-resource devices pose a huge threat to sensitive human data. This paper introduces an artificial immune system (AIS) based approach to intrusion detection in IoT network ecosystems. The proposed approach implements dual-layered AIS; which is robust to zero-day attacks and designed to adapt new types of attack classes in the form of antibodies. In this paper, a hybrid method has been presented which uses hybrid of clonal selection using variational auto-encoders as innate immune layer and apaptive dentritic model for identifying intrusions over IoT specific datasets. Moreover we present extensive empirical analysis over six IoT network benchmark datasets for semi-supervised multi-class classification task and obtain superior performance compared to five state-of-the-art baselines. Finally, VC-ADIS achieves 99.83% accuracy over MQTT-set dataset.