Healthcare Intrusion Detection using Hybrid Correlation-based Feature Selection-Bat Optimization Algorithm with Convolutional Neural Network A Hybrid Correlation-based Feature Selection for Intrusion Detection Systems

Abstract

Cloud computing is popular among users in various areas such as healthcare, banking, and education due to its low-cost services alongside increased reliability and efficiency. But, security is a significant problem in cloud-based systems due to the cloud services being accessed via the Internet by a variety of users. Therefore, the patient’s health information needs to be kept confidential, secure, and accurate. Moreover, any change in actual patient data potentially results in errors during the diagnosis and treatment. In this research, the hybrid Correlation-based Feature Selection-Bat Optimization Algorithm (HCFS-BOA) based on the Convolutional Neural Network (CNN) model is proposed for intrusion detection to secure the entire network in the healthcare system. Initially, the data is obtained from the CIC-IDS2017, NSL-KDD datasets, after which min-max normalization is performed to normalize the acquired data. HCFS-BOA is employed in feature selection to examine the appropriate features that not only have significant correlations with the target variable, but also contribute to the optimal performance of intrusion detection in the healthcare system. Finally, CNN classification is performed to identify and classify intrusion detection accurately and effectively in the healthcare system. The existing methods namely, SafetyMed, Hybrid Intrusion Detection System (HIDS), and Blockchain-orchestrated Deep learning method for Secure Data Transmission in IoT-enabled healthcare systems (BDSDT) are employed to evaluate the efficacy of HCFS-BOA-based CNN. The proposed HCFS-BOA-based CNN achieves a better accuracy of 99.45% when compared with the existing methods: SafetyMed, HIDS, and BDSDT.