Threat Intelligence Model to Secure IoT based Body Area Network and Prosthetic Sensors

Abstract

This research work proposes a threat intelligence model for IoT sensors based Body Area Network (BAN) majorly used in healthcare of critically ill patients and on the contrary performance measurement system for healthy sports persons. The end point control based applications are growing enormously with the advent of IoT based sensors and actuators being used in intelligent real-time systems. At the same time keeping the environment safe for the user is expected to be delivered with constant updates. The process for the monitoring of health and wellness of a person, or measuring endurance and performance of a sports person remains vulnerable without a secure environment. The entire system may be designed for the personalized medication of a patient with the sophisticated device like prosthetic heart valve or even the personal safety for the elderly people in the society to prevent fall due to muscle weakness [2]. The security of each and every act in the dimension of internet of things needs attention towards the reduction in the cyber-attacks on IoT based systems. To maintain the confidentiality of patient, or health and wellness records of a sports-person, security and privacy are the most crucial dimensions to be managed inevitably. The proposed light weight threat intelligence model enables the incorporation of secure tunnelling for the hash function to intelligently detect the anomaly and manage the breach. The proposed multi-layered outlier detection model incorporates the unified threat management for the body area network along with prosthetic sensor implanted in the person like heart valves or performance aggregators for sports person. The seamless confluence of mist computing with the body area network enables an outbound and inbound performance management of signals shared among entire body area network for the patient. The IoT devices and sensors get an extra layer of security for the environment without compromising the signal comprehension amongst the healthcare process delivery. The intrusion divergence process for the inbound security never let the alien signals breach the secure environment for the body area network under surveillance. The major bottlenecks comprise of the factors, that IoT devices especially Bio-sensors are composed with low powered energy source and less memory for processing [1]. The sports persons are nowadays smartly trained to remain fit and avert any kind of adverse situations, for the same they keep themselves many a times equipped with bio-sensors to monitor all the required vitals during training. The player may be wearing the near field communication devices which could populate the server with the vital signals from the body parts [4]. The threat management along with energy conservation redirects our proposed model towards the usage of Qi-Powered multimodal architecture for the body area network [2]. The Qi based interface is capable to resolve various bottlenecks in the phases of the secure system implementation, like managing passive intermediate relays to route the signals across the body area network [2]. The Near Field Communication devices in the network shall be guided through gazelle protocol to enable the multimodal data communications and in turn the scalability of IoT devices or sensors in the network along with Bi-directional signal processing capabilities [2].