@inproceedings{104a0a41bcb142179dd7be07a2b8b545,
title = "A Novel Deep Learning and Feature Optimization Approach for Detecting Mobile Phishing Attacks in Smart Agriculture Systems",
abstract = "Smart agriculture, connecting the Internet of Things with mobile devices and computing systems, represents a new technology that shows improvements in farming. However, this growth causes significant security challenges, as phishing attacks often target IoT devices to exploit weaknesses and generate false alerts. Our study focuses on combining statistically optimized characteristics with a lightweight, deep learning model designed especially for mobile phishing detection in smart agriculture systems. Research conducted on a real-world phishing dataset showed outstanding efficiency, achieving 98\% accuracy.",
keywords = "deep learning, optimization, phishing attack, smart agriculture",
author = "Mosiur Rahaman and Jinsong Wu and Hsu, \{Ching Hsien\} and Chui, \{Kwok Tai\} and Gupta, \{Brij B.\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 14th IEEE Global Conference on Consumer Electronics, GCCE 2025 ; Conference date: 23-09-2025 Through 26-09-2025",
year = "2025",
doi = "10.1109/GCCE65946.2025.11275005",
language = "English",
series = "GCCE 2025 - 2025 IEEE 14th Global Conference on Consumer Electronics",
pages = "599--600",
booktitle = "GCCE 2025 - 2025 IEEE 14th Global Conference on Consumer Electronics",
}