Health-Related Sensor Data Infection Detection Using Machine Learning

Abstract

Small modifications in the virus code are easily detected by conventional signature-based malware detection techniques. The majority of malware programs nowadays are modifications of other applications. They thus have various signatures yet have certain similar patterns. Instead than only seeing slight changes, it's important to recognize the virus pattern in order to protect sensor data. However, we suggest a quick detection technique to find patterns in the code using machine learning-based approaches in order to quickly discover these health sensor data in malware programs. To evaluate the code using health sensor data, XGBoost, LightGBM, and Random Forests will be specifically used. The codes are either supplied into them as single bytes or tokens or as sequences of bytes or tokens (e.g. 1-, 2-, 3-, or 4- grams).