Optimized intrusion detection for IoT networks using Cauchy-Gaussian hybrid evolutionary feature selection.

ABSTRACT: The Internet of Things network is a prime target for attackers due to its vulnerabilities and the sensitive data it handles. Protecting these devices is critical, and an Intrusion Detection System serves as the first defence against breaches. While many intrusion classification methods exist, building low-complexity systems for IoT remains challenging. This paper introduces a novel method combining active feature selection and ensemble machine learning to address complexity issues for IDS in IoT. Specifically, a novel Cauchy-Gaussian genetic-arithmetic optimiser-driven variance-based active feature selection method is proposed. The proposed algorithm operates in two phases: in the first phase, the model learns active samples by representing them as a KD-tree based on feature variance; in t

SUBMITTER: Saranya T 

PROVIDER: S-EPMC12775014 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature