Sustainable IoT Security in Entrepreneurship: Leveraging Univariate Feature Selection and Deep CNN Model for Innovation and Knowledge

Brij B. Gupta (), Akshat Gaurav, Razaz Waheeb Attar, Varsha Arya, Ahmed Alhomoud and Kwok Tai Chui
Additional contact information
Brij B. Gupta: Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
Akshat Gaurav: Computer Science and Engineering, Ronin Institute, Montclair, NJ 07043, USA
Razaz Waheeb Attar: Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Varsha Arya: Department of Business Administration, Asia University, Taichung 413, Taiwan
Ahmed Alhomoud: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
Kwok Tai Chui: Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University (HKMU), Hong Kong

Sustainability, 2024, vol. 16, issue 14, 1-13

Abstract: Due to the rapid increase in Internet of Things (IoT) devices in entrepreneurial environments, innovative cybersecurity advancements are needed to defend against escalating cyber threats. The present paper proposes an approach involving univariate feature selection leading to Sustainable IoT security. This method aims at increasing the efficiency and accuracy of the deep Convolutional Neural Network (CNN) model concerning botnet attack detection and mitigation. The approach to obtaining Sustainable IoT Security goes beyond the focus on technical aspects by proving that increased cybersecurity in IoT environments also fosters entrepreneurship in terms of stimulation, knowledge increase, and innovation. This approach is a major step towards providing entrepreneurs with the necessary tools to protect them in this digital era, which will enable and support the defense against cyber threats. A secure, innovative, and knowledgeable entrepreneurial environment is the result of Sustainable IoT security.

Keywords: IoT security; botnet detection; deep learning; feature selection; entrepreneurship (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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