Hybrid stacked autoencoder with dwarf mongoose optimization for Phishing attack detection in internet of things environment

Abstract

With the fast development of the Internet of Things (IoTs), phishing attacks are transferring to this domain because of the number of IoT devices and private data they can handle. IoT phishing suggests the malicious practice of targeting IoT devices and their users to steal sensitive data, gain unauthorized access, or compromise the safety and functionality of these devices. Phishing attacks in the framework of IoT take several procedures, comprising SMS phishing, email phishing, or even physical attacks on the devices themselves. Deep learning (DL) approaches are executed to improve the prevention and detection of IoT phishing attacks. Consequently, this article introduces a Dwarf Mongoose Optimization with deep learning for Phishing Attack Detection (DMODL-PAD) method in an IoT platform. The objective of the DMODL-PAD method is to utilize feature selection (FS) with a hyperparameter tuning strategy for automated phishing attack detection in the IoT platform. The DMODL-PAD method involves the DMO model, unlike the FS method. Afterwards, a hybrid stacked autoencoder (HSAE) model can execute the phishing attack detection process. The DMODL-PAD method uses a jellyfish search optimizer (JSO) for the hyperparameter tuning process. The experimentation validation of the DMODL-PAD technique has been examined under a benchmark database. The extensive outcomes portrayed the superior detection results of the DMODL-PAD technique over other recent methods. These results ensured that the DMODL-PAD technique could effectually detect phishing attacks from the IoT environment.

Keywords

• Internet of Things
• Cybersecurity
• Deep learning
• Metaheuristics
• Hyperparameter tuning