Securing IoT Healthcare Data Using Machine Learning Techniques

The internet of Things has become a crucial technology, enabling the interconnection of everyday devices like home appliances, medical equipment and vehicles. However, the sensitive data handled by IoT systems, particularly in the healthcare sector, requires robust security measures to prevent tampering or theft. Detecting security threats in IoT environments requires sophisticated techniques. Different machine learning algorithms are used to identify the attacks in IoT network traffic and predict snooping behavior based on unidentified patterns. This paper proposed a method to identify network traffic attacks by employing different strategies utilizing the NT-ToN-IoT dataset. The classifier utilized Naïve Bayes(NB), Random Forest(RF), Support Vector Machine, Artificial Neural Network. These algorithms favoured over centralized methods to construct streamlined security system for IoT devices. Prior to analysis the dataset underwent preprocessing to eliminate irrelevant or missing data. Following this, a feature engineering approach is applied to extract crucial features. The results from applying each classifier demonstrated a significant maximum classification accuracy of 98% achieved by the RF model.