Strategic Approaches to Intrusion Detection in Cloud-IoT Ecosystem

6Smart Shields: Machine Learning Approaches for Adaptive Defense in Cloud-IoT Security

Bhupendra Panchal^1*, Aafiya Choudhary¹, Ashish Anand¹, Ajay Sharma¹ and Tarannum Khan²

¹School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, Madhya Pradesh, India

²Computer Science and Engineering, Mahakal Institute of Technology, Ujjain, Madhya Pradesh, India

Abstract

In today’s interconnected world, traditional security measures are increasingly inadequate for safeguarding the rapidly expanding cloud-based Internet of Things (IoT) networks against sophisticated cyber threats. This paper presents “Smart Shields,” an innovative security framework that leverages advanced machine learning (ML) techniques to provide adaptive and real-time defense for IoT ecosystems. Our approach incorporates various ML algorithms, such as Decision Trees, Support Vector Machines, Random Forests, and Principal Component Analysis, to accurately identify and respond to anomalies in IoT networks. To address the limitations of conventional centralized security solutions—high latency, power consumption, and poor scalability—our framework employs a distributed model using fog computing, enhancing threat detection efficiency at the network edge. By integrating advanced ML models and an autonomous, continuously learning security management system, Smart Shields dynamically adapts to new threats, ensuring robust protection of IoT environments. This research demonstrates that Smart Shields ...