Using Machine Learning Detection Malware in IoHT System

The Internet of Health Things (IoHT) is a network of healthcare equipment, software, and systems that enable remote monitoring and healthcare services. Real-time health data are gathered via sensors. Even IoHT offers many benefits for modern smart healthcare, security concerns are increasing since IoHT devices lack appropriate processing power, storage capacity, and self-defense capabilities. In the healthcare sector, the use of Machine Learning (ML) for malware detection is vital for saving patients sensitive data. Therefore, it is essential to improve the accuracy and effectiveness of detection methods. ML models have been utilized to enhance the efficiency of malware detection. The main objectives of the attackers are to obtain personal information and take advantage of device flaws. Scientists are also devising diverse methods for identifying and analyzing malware to address these challenges. Given the continuous introduction of new malware by developers, it is highly tough to construct comprehensive algorithms for detecting such malware. Researchers have developed several ML and Deep Learning (DL) algorithms. The precision of these models will mainly contingent upon the amount of the training dataset. In addition, our work is divided into three primary stages: feature selection, prediction, and pre-processing. This work introduces feature selection technique that integrates two approaches, the first one Pearson correlation, to assess the correlation between features and identify significant features and Embedded method. These selected features are subsequently utilized in a classification model. Our method utilizes a soft voting classifier that combines multiple machine learning models (decision tree, logistic regression, gradient boost, random forest, and support vector machine) to detect malware. This approach creates a single model that incorporates the strengths of the combined models, resulting in the highest prediction accuracy. The proposed methodology surpasses previous research by reaching a 99.6% accuracy rate, an F1 score of 0.9972% a recall rate of 0.9998, a precision rate of 0.9947.