Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment

Amit Sagu, Nasib Singh Gill, Preeti Gulia, Pradeep Kumar Singh and Wei-Chiang Hong ()
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Amit Sagu: Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, India
Nasib Singh Gill: Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, India
Preeti Gulia: Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, India
Pradeep Kumar Singh: School of Technology Management & Engineering, Narsee Monjee Institute of Management Studies (NMIMS), Chandigarh 160014, India
Wei-Chiang Hong: Department of Information Management, Asia Eastern University of Science and Technology, New Taipei 22046, Taiwan

Sustainability, 2023, vol. 15, issue 3, 1-21

Abstract: Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.

Keywords: deep learning models; IoT security; optimization algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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