Work

A multi-level intrusion detection system for industrial IoT using bowerbird courtship-inspired feature selection and hybrid data balancing

Year: 2025

Type: article

Abstract: Industrial Internet of Things (IIoT) deployments demand intrusion‑detection systems (IDSs) that are both accurate and lightweight. We propose a two‑tier IDS in which a Logistic Regression (LR) model running at the edge classifies the bulk of traffic, while about 16% of low‑confidence flows are forwarded to a cloud‑hosted 1‑D Convolutional Neural Network (CNN) for deeper inspection. A Bowerbird Courtship‑Inspired Feature Selection (BBFS) algorithm reduces the input space at both tiers—using a recall‑weighted F1 wrapper for LR and a Fisher‑score & correlation‑penalty filter for the CNN—while a hybrid SMOTE+ENN+LOF strategy mitigates class imbalance. On the WUSTL‑IIoT data set, the edge model with balancing plus BBFS attains 99.70% accuracy and 99.71% recall, and the cloud CNN reaches approximately 100.00% accuracy with 99.99% precision and a 99.98% F1‑score. Feature reduction lowers LR inference from 52 ms to 38 ms and CNN inference from 27.86 s to 18.28 s on a Raspberry Pi 4 B, demonstrating the framework’s suitability for real‑time, resource‑constrained IIoT security.

Source: Discover Computing

DOI: 10.1007/s10791-025-09632-z

Authors: S Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

Institutions: Jawaharlal Nehru Technological University, Kakinada, Jawaharlal Nehru Technological University-Gurajada, Vizianagaram

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,520