Comprehensive Insights into Machine Learning for Intrusion Detection Systems in IoT and its Datasets

The Internet of Things' (IoT) explosive growth has completely changed how we interact with technology by joining billions of devices—from industrial sensors to smart thermostats—to the global network. Although there are many advantages to this interconnectedness, there are also serious security concerns. Intrusion detection systems (IDS) have become indispensable tools in response to these concerns. They work nonstop to protect the network from intrusions by closely examining network traffic to guarantee its integrity, confidentiality, and availability. Intrusion detection systems (IDS) continue to face difficulties in increasing detection accuracy, decreasing false alarms, and successfully identifying new intrusion patterns in spite of the devoted efforts of researchers. Cyber threat protection for IoT ecosystems is still a major concern, and machine learning (ML)-powered IDS have become more prevalent.