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A Hybrid and Adaptive Architecture for Explainable Intrusion Detection in Constrained IoT Environments
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 130 |
| Year of Publication: 2026 |
| Authors: Nkondock Mi Bahanag Nicolas, Bayem Jacques Narcisse, Atsa Etoundi Roger |
10.5120/ijcac0c807312efa
|
Nkondock Mi Bahanag Nicolas, Bayem Jacques Narcisse, Atsa Etoundi Roger . A Hybrid and Adaptive Architecture for Explainable Intrusion Detection in Constrained IoT Environments. International Journal of Computer Applications. 187, 130 ( Jul 2026), 21-30. DOI=10.5120/ijcac0c807312efa
Abstract
Internet of Things (IoT) environments are increasingly vulnerable to cyberattacks, necessitating effective and explainable intrusion detection solutions. This paper proposes a hybrid and adaptive architecture for explainable intrusion detection in constrained IoT environments. The approach combines workflow mining techniques with security rule-based detection to identify intrusions. It allows to avoid false positives during intrusion detection. Here, explainability also use methods to provide insights into detection decisions. More, the solution is hybrid due to the use of several techniques and also because it allows the detection of intrusions locally or through the Internet. The defined architecture offers advantages such as scalability and flexibility.
References
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