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An Efficient Hybrid Approach Based on Deep Learning and Stacking Ensemble Using the Whale Optimization Algorithm for Detecting Attacks in IoT Devices

Year: 2025

Type: article

Abstract: ABSTRACT With the increasing proliferation of the Internet of Things (IoT) and the rise of cyber‐attacks targeting these devices, the need for efficient attack detection methods has become increasingly urgent. In this paper, a novel approach is proposed based on ensemble deep transfer learning optimized using the Whale Optimization Algorithm (WOA) to detect attacks on IoT devices. The method employs multiple ensemble deep transfer learning (DTL) models that transfer knowledge from data in other domains to IoT‐specific data. Subsequently, the WOA algorithm is utilized to optimize the model parameters and combine them into an ensemble classifier. The performance of the proposed method is evaluated using the Edge‐IIoTset dataset, and the results demonstrate a detection accuracy of 99.6%, with a significantly reduced false alarm rate. These findings highlight the high efficiency of the proposed method in enhancing the security of IoT devices.

Source: Engineering Reports

DOI: 10.1002/eng2.70338

Authors: Reza Tavoli, Elaheh Rezvani, Mirsaeid Hosseini Shirvani

Institutions: Islamic Azad University of Chalous, Islamic Azad University Sari Branch

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

OpenAlex ID: W4414033263

FWCI: 4.475

Cites: 33

Cited by: 10

Related to: 10