Browsing by Author "Attiya Khan"

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    Intrusion Detection in IoMT based Smart Healthcare System (SHS) using Deep Learning techniques
    (KINNAIRE COLLEGE COMPUTER SCIENCE DEPARTMENT, 0022-06-23) Attiya Khan
    The Internet-of-Things (IoT) has infiltrated nearly every aspect of life. One of the most important areas where IoT solutions and infrastructures are used widely is smart healthcare system (SHS). IoT-based smart healthcare solutions have significantly increased the benefit of the healthcare sector with the use of mobile and wearable devices. Smart healthcare reduces hospitalization costs and provides timely treatment for a number of medical conditions by incorporating IoT sensors into health monitoring equipments. Today, the purpose of healthcare systems is not confined to treating patients only. In SHS, wearables, implantable devices, and sensors monitor the vital parameters of a patient. These parameters are sent for evaluation to the emergency services or healthcare professionals. This results in a significant usage of health data exchange for improved, timely, and more accurate diagnosis. Nevertheless, SHS are extremely prone to a variety of security breaches and malicious attacks, such as tampering, privacy leakage, and forgery. In the smart healthcare domain, it is essential to take a systematic approach to privacy and security measures in communication, data storage, interconnecting things, and data handling. In various studies, several intrusion detection systems (IDS) have been proposed to detect cyber security threats in SHS and to identify malicious attacks and privacy breaches. This study was conducted as a consequence of the limits of IDSs in responding to challenges and attacks and in implementing attacks and privacy access control in the SHSs. In this study, we designed a deep learning-based intrusion detection system to efficiently identify smart healthcare network intrusions by evaluating traffic flow data. We specifically used the “Long Short-Term Memory (LSTM)” technique to detect malicious attacks and other security threats in SHS. In this system, we utilized the CFS algorithm for feature selection. The objective is to select a subset of features having a high feature-class correlation in order to maintain or boost predictive power, and low feature-feature correlation to prevent redundancy. We evaluated the proposed system using Wustl-ehms-2020 IoMT dataset. The proposed system achieves accuracy of 96%, which is greater than existing v approaches. This study demonstrates that our approach outperforms other cutting-edge techniques for intrusion