TY - JOUR
T1 - Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Arya, Varsha
AU - Attar, Razaz Waheeb
AU - Bansal, Shavi
AU - Alhomoud, Ahmed
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
Copyright © 2024 The Authors.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - ANOVA F-test
KW - cable news network (CNN)
KW - Cuckoo Search
KW - Deep learning
KW - IoT
KW - phishing
UR - http://www.scopus.com/inward/record.url?scp=85212814879&partnerID=8YFLogxK
U2 - 10.32604/cmc.2024.056476
DO - 10.32604/cmc.2024.056476
M3 - Article
AN - SCOPUS:85212814879
SN - 1546-2218
VL - 81
SP - 4109
EP - 4124
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 3
ER -