DefinePK

Abstract

Iintrusion deduction systems (IDS) are crucial to preserving sensitive medical information from cyber threats. However, issues with multi-class intrusion detection include an imbalanced data 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 methods to 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 issues in multi-class Intrusion detection by exhibiting remarkable precision for minority classes and consistent performance across all categories. These results highlight the framework's effectiveness in providing dependable and effective normal detection solutions, which makes it ideal for implementation in crucial sectors like healthcare, their accuracy and data security are crucial.