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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
  • , Kwok Tai Chui

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number6219
JournalSustainability (Switzerland)
Volume16
Issue number14
DOIs
Publication statusPublished - Jul 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • IoT security
  • botnet detection
  • deep learning
  • entrepreneurship
  • feature selection

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