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

• Autorzy: Soni Vineeta , Bhatt Devershi Pallavi , Yadav Narendra Singh

Identyfikatory

DOI

10.24425/bpasts.2024.152211

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Internet of Things; artificial immune system; variational clonal selection; IoT security

PL

Internet rzeczy; sztuczny układ odpornościowy; bezpieczeństwo IoT; IoT; selekcja klonalna wariacyjna

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2025
• Tom: Vol. 73, nr 1
• Strony: art. no. e152211
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr.

Twórcy

autor

Soni Vineeta

• Manipal University Jaipur, Jaipur, India

• https://orcid.org/0000-0003-2539-052X

autor

Bhatt Devershi Pallavi

• Manipal University Jaipur, Jaipur, India

autor

Yadav Narendra Singh

• Manipal University Jaipur, Jaipur, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).