Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA) based Support Vector Machine (SVM) for Cyberattack Detection in Healthcare

Usage of Internet of Things (IoT) in medical area is defined as IoMT that plays a signif-icant part in exchanging of sensitive information between medical systems. Though, that comes with privacy securities which comprised the security of information gathered through medical sensors, made them susceptible for potential cyber issues like modification of data, attacks re-play. These attacks can cause important information loss or unauthorized modifications. In this research, a Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA) based Support Vector Machine (SVM) is proposed for cyberattack detection. The dataset used in research is WUSTL-EHMS-2020 which is healthcare dataset. The dataset is pre-processed through label encoding and data normalization. Then feature extraction is performed by Kernal Partial Least Square (KPLS) Method and detection is performed through GBBOA based SVM. The perfor-mance of proposed method is analysed with performance measures of accuracy, precision, re-call and f1-score. The proposed method attained accuracy 97.72%, precision 97.03%, recall 96.56% and f1-score 96.84% which is efficient than other existing methods like Particle Swarm Optimization – Machine learning (PSO – ML) and ensemble Machine Learning (ML) methods.