Corresponding Author

Mahmoud gad awwad

Subject Area

Electronics and Communication Engineering

Article Type

Original Study


As the Internet of Things (IoT) continues to expand, ensuring the security and privacyِ of IoT systems becomes increasingly critical. Phishing attacks pose a significant threat to IoT devices and can lead to unauthorized access, data breaches, and compromised functionality. In this paper, we propose an anti-phishing approach for IoT systems in fog networks that leverages machine learning algorithms, including a .fusion with deep learning techniques We explore the effectiveness of eleven traditional machine learning algorithms combined with deep learning in detecting and preventing phishing attacks in IoT systems. By utilizing a diverse range of algorithms, we aim to enhance the accuracy .of our proposed approach To evaluate the performance of our approach, we conducted experiments using three distinct datasets to identify which dataset yields the highest accuracy. The datasets encompass various real-world scenarios and consist of phishing instances targeting .IoT devices Our experimental results demonstrate an impressive accuracy rate of 99.37\% in detecting and mitigating phishing attacks in IoT systems. This achievement highlights the effectiveness of our approach in providing robust security measures for fog .networks The findings from this research contribute to the advancement of anti-phishing techniques for IoT systems, offering a valuable defense against evolving cyber threats. By leveraging a combination of traditional machine learning algorithms and deep learning, our approach exhibits strong potential for practical implementation in real-world IoT deployments.


Phishing attack, Machine learning, deep learning, Feature selection, Random forest, Fog computing

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.