NeuroSecure-IoMT: Deep Learning Meets Cyber Defense in the Internet of Medical Things

Intrusion deduction systems (IDS) are crucial to preservingsensitive medical information fromcyber threats. However, issueswith multi-class intrusion detection include an imbalanceddata set, poor accuracy for minority classes,and a lack of flexibility in handling complex real-world situations. To address these issues, we provide a hybrid framework that combines machine learning and deep learning methodsto address these problems. The model uses a random forest classifier for anomaly detection after reducing dimensionality using an autoencoder. The Synthetic Minority Oversampling Technique (SMOTE) was used during processing to ensure equitable class representation and reduce class imbalance. A multi-class intrusion detection dataset tailored to healthcare applications was used to thoroughly test the suggested framework, which provides an impressive 99% accuracy rate. In addition to its excellent accuracy, the model addresses important issuesin multi-class Intrusion detection by exhibiting remarkable precision for minority classes and consistent performance across all categories. These results highlight the framework'seffectiveness in providing dependable and effective normal detection solutions, which makesit ideal for implementation incrucial sectors like healthcare, their accuracy and data security are crucial.