AI-Driven Adaptive Cybersecurity: Toward Dynamic Threat Detection Across IoT, Cloud, and Smart Manufacturing Environments
Author(s): Mohammad Ayan Khan, Ashish Kumar Jain
Publication #: 2507040
Date of Publication: 14.12.2019
Country: India
Pages: 1-11
Published In: Volume 5 Issue 6 December-2019
Abstract
The increasing interconnectivity brought by the Internet of Things (IoT), cloud computing, and Industry 4.0 has significantly amplified cybersecurity risks across multiple sectors, including healthcare, automotive, and smart manufacturing. Traditional cybersecurity solutions, largely static and signature-based, are inadequate against the sophisticated and rapidly evolving nature of modern cyber threats. This paper investigates the integration of artificial intelligence (AI)-driven adaptive cybersecurity frameworks capable of dynamic threat detection and mitigation across heterogeneous environments. By synthesizing existing literature on secure software development, functional safety, cybersecurity standards (ISO 26262, ISO/SAE 21434), and ontology-based IoT security models, we highlight the gaps in current methodologies, particularly in real-world adaptability and interoperability. We propose a holistic AI-enhanced cybersecurity approach that combines machine learning, real-time monitoring, and automated response mechanisms tailored for IoT, cloud infrastructures, and smart factories. Additionally, we emphasize the importance of scalable cyber ranges for training and the need for interdisciplinary cybersecurity education to develop a resilient workforce. Future work should focus on implementing digital twin simulations, developing unified safety-security frameworks, and creating country-specific policies to fortify digital infrastructures against emerging threats.
Keywords: AI-Driven Cybersecurity, Dynamic Threat Detection, IoT Security, Cloud Computing, Smart Manufacturing, Adaptive Security Frameworks
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