Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

被引：0

作者：

Gupta, Brij B. ^{[1
,2
,3
,4
]}

Gaurav, Akshat ^{[5
]}

Arya, Varsha ^{[6
,7
]}

Attar, Razaz Waheeb ^{[8
]}

Bansal, Shavi ^{[9
]}

Alhomoud, Ahmed ^{[10
]}

Chui, Kwok Tai ^{[11
]}

机构：

[1] Department of Computer Science and Information Engineering, Asia University, Taichung

[2] Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Maharashtra, Pune

[3] Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun

[4] University Centre for Research and Development (UCRD), Chandigarh University, Chandigarh

[5] Computer Engineering, Ronin Institute, Montclair, 07043, NJ

[6] Department of Business Administration, Asia University, Taichung

[7] Department of Electrical and Computer Engineering, Lebanese American University, Beirut

[8] Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh

[9] Department of Research and Innovation, Insights2Techinfo, Jaipur

[10] Department of Computer Science, Faculty of Science, Northern Border University, Arar

[11] Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University (HKMU)

来源：

Computers, Materials and Continua | 2024年 / 81卷 / 03期

关键词：

ANOVA F-test; cable news network (CNN); Cuckoo Search; Deep learning; IoT; phishing;

D O I：

10.32604/cmc.2024.056476

中图分类号：

学科分类号：

摘要：

Phishing attacks seriously threaten information privacy and security within the Internet of Things (IoT) ecosystem. Numerous phishing attack detection solutions have been developed for IoT; however, many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application. This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection. Our model employs a two-fold optimization approach: first, it utilizes the analysis of the variance (ANOVA) F-test to select the optimal features for phishing detection, and second, it applies the Cuckoo Search algorithm to tune the hyperparameters (learning rate and dropout rate) of the deep learning model. Additionally, our model is trained in only five epochs, making it more lightweight than other deep learning (DL) and machine learning (ML) models. The proposed model achieved a phishing detection accuracy of 91%, with a precision of 92% for the’normal’ class and 91% for the ‘attack’ class. Moreover, the model’s recall and F1-score are 91% for both classes. We also compared our approach with traditional DL/ML models and past literature, demonstrating that our model is more accurate. This study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection. Copyright © 2024 The Authors.

引用

页码：4109 / 4124

页数：15

共 32 条

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