This research demonstrates how Machine Learning (ML) and Deep Learning (DL) techniques can overcome limitations of traditional Intrusion Detection Systems (IDS) for IoT environments.

• Evaluates various ML/DL approaches for detecting intrusions in dynamic IoT networks
• Categorizes IDS deployment strategies specifically optimized for IoT contexts
• Addresses challenges of securing resource-constrained IoT devices at scale
• Provides a framework for selecting appropriate ML techniques based on threat types

As IoT adoption accelerates across industries, this research offers critical insights for security professionals seeking to implement robust protection against evolving threats in increasingly complex connected ecosystems.

Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things