An Approach for Detecting Security Attacks using Machine Learning in IoT Environment

Abstract

The strong security measures are becoming even more necessary to protect these networked systems as a result of the proliferation of Internet of Things (IoT) devices. The dynamic and varied nature of IoT networks frequently makes it impossible for traditional security solutions to be effective. The method suggested in this research uses machine learning to identify security attacks in IoT contexts. The suggested method makes use of the capabilities of machine learning algorithms to examine the enormous amounts of data produced by IoT devices. The system can develop the ability to recognise possible security threats and take immediate action by training models on labelled datasets that include both normal and attack patterns. In this paper multiple ML models for security threat detection in IoT environments in this study. Our evaluation uses both binary and multiclass classification models in an effort to accurately reflect the variety of assaults that can be found in IoT environments. The proposed method provide a new specialised IoT dataset that was created especially to reflect the characteristics of actual IoT environments in order to assure the validity of our assessment. By completing this extensive analysis, we hope to shed light on how well AI-based methods for security attack detection in IoT contexts work. The results of this study can help researchers and professionals decide which ML models and feature engineering techniques are best for IoT security. At the end of the day, we want to help with the creation of reliable security systems that safeguard IoT devices and shield user data from harmful attacks.