Abstract: The convergence of artificial intelligence (AI), software-defined networking (SDN), and cybersecurity has opened new avenues for protecting critical infrastructure systems against increasingly sophisticated cyber threats. This review explores how emerging entrepreneurial ventures are pioneering AI-driven solutions for anomaly detection within SDN architectures, particularly in sectors like energy, transportation, and healthcare. By decoupling control and data planes, SDN offers centralized programmability that enhances network agility but also introduces new vulnerabilities. AI techniques—such as machine learning, deep learning, and neural networks—have been adopted by cybersecurity startups to build scalable and adaptive anomaly detection frameworks that mitigate these vulnerabilities in real time. This paper examines the landscape of entrepreneurial innovation, highlighting case studies of startups that leverage AI to enhance threat detection, automate response mechanisms, and provide predictive security analytics in SDN environments. Emphasis is placed on the integration of zero-trust principles, edge intelligence, and decentralized detection architectures. Furthermore, the paper analyzes the commercial viability, scalability, and deployment challenges of these solutions, as well as their implications for regulatory compliance and cyber resilience. The findings highlight the growing significance of entrepreneurial ecosystems in driving technological advancements that safeguard critical infrastructure against evolving cyber risks.
Keywords: AI-Driven Anomaly Detection; Software-Defined Networking (SDN); Critical Infrastructure Security; Cybersecurity Entrepreneurship; Zero-Trust Architecture.
Title: Entrepreneurial Innovations in AI-Driven Anomaly Detection for Software-Defined Networking in Critical Infrastructure Security
Author: Chima Nwankwo Idika, Joy Onma Enyejo, Onuh Matthew Ijiga, Nonso Okika
International Journal of Social Science and Humanities Research
ISSN 2348-3156 (Print), ISSN 2348-3164 (online)
Vol. 13, Issue 3, July 2025 - September 2025
Page No: 150-166
Research Publish Journals
Website: www.researchpublish.com
Published Date: 24-July-2025
