@ARTICLE{Soni_Vineeta_HAIS-IDS:_2025,
 author={Soni, Vineeta and Bhatt, Devershi Pallavi and Yadav, Narendra Singh},
 volume={73},
 number={1},
 pages={e152211},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 howpublished={online},
 year={2025},
 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.},
 title={HAIS-IDS: A hybrid artificial immune system model for intrusion detection in IoT},
 type={Article},
 URL={http://czasopisma.pan.pl/Content/133048/PDF-MASTER/BPASTS_2025_73_1_4471.pdf},
 doi={10.24425/bpasts.2024.152211},
 keywords={Internet of Things, artificial immune system, variational clonal selection, IoT Security},
}