| Title |
Sustainable IoT Security in Entrepreneurship: Leveraging Univariate Feature Selection and Deep CNN Model for Innovation and Knowledge |
| ID_Doc |
70411 |
| Authors |
Gupta, BB; Gaurav, A; Attar, RW; Arya, V; Alhomoud, A; Chui, KT |
| Title |
Sustainable IoT Security in Entrepreneurship: Leveraging Univariate Feature Selection and Deep CNN Model for Innovation and Knowledge |
| Year |
2024 |
| Published |
Sustainability, 16, 14 |
| DOI |
10.3390/su16146219 |
| 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. |
| Author Keywords |
IoT security; botnet detection; deep learning; feature selection; entrepreneurship |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:001277582500001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://doi.org/10.3390/su16146219
|