Abstract
Despite various advances in health care applications, Intrusion detection remains a challenging task in wireless network due to numerous amount of traffic and large amount of sensitive data leading to various cyber attacks (DDoS, MITM, scan etc).The sensors are used to collect the real time data of patients or wearable devices and send the same to doctors for analyzing various diseases in early stages using IoT. Wireless network is vulnerable to intruders ,mainly for carrying sensitive information hence this paper proposes supervised machine learning technique B-GNB to detect various attacks in Health care data in IuT.The proposed model chooses the best features and accurately classifies attacks with 96% accuracy.
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