Abstract
At present, network security is the most important subject matter because with the rapid use of internet technology to exchange the data, which becoming intolerable to protect the data from vulnerable attacks. Therefore, intrusion detection systems (IDS) appeared as the key solution for detecting these attacks so that the network remains reliable. Several IDSs are available but the main issue is their performance, which can be enhanced by increasing the detection rates (DR) and reducing false alarm rates (FAR). In this research work, a new approach is presented in order to solve performance issue in healthcare information systems. For classification purpose, Recurrent Neural Network (RNN) is proposed in order to enhance performance issue. NSL-KDD Dataset is used for evaluation and assessment. Moreover, Principle Component Analysis (PCA) is applied in this work in order to project features space to principal feature space and choose features corresponding to the highest eigenvalues. Our approach is capable of enhancing performance by increased DR and decreased FAR.