Intrusion Detection in IOT Networks using Machine Learning Techniques

Abstract

Artificial intelligence (AI) and machine learning (ML) are essential for processing vast datasets and forecasting unknown events, offering innovative solutions to IoT security challenges. Recurrent neural networks (RNNs) have extended the predictive capacity of traditional neural networks, particularly in forecasting sequential events. With the increasing frequency of system attacks, the integration of machine learning into intrusion detection systems (IDS) is vital to identify and report potential threats, thereby safeguarding IoT infrastructure against destructive attacks