{"id":49985,"date":"2024-09-27T10:30:53","date_gmt":"2024-09-27T05:00:53","guid":{"rendered":"https:\/\/www.jaipuria.ac.in\/blog\/?p=48818"},"modified":"2024-09-27T10:30:53","modified_gmt":"2024-09-27T05:00:53","slug":"can-the-threat-intelligence-model-protect-patient-records","status":"publish","type":"post","link":"https:\/\/www.jaipuria.ac.in\/blog\/can-the-threat-intelligence-model-protect-patient-records\/","title":{"rendered":"Can the Threat Intelligence Model Protect Patient Records?"},"content":{"rendered":"

Can the Threat Intelligence Model secure the confidentiality of the Health and Wellness Records of the Patients? \u2013 Research by Dr. Deepak Singh<\/a> from Jaipuria Noida<\/a><\/h3>\n

Cyber-attacks are rated among the top five risks in 2020, expected to grow twice by 2025, and the detection rate is as low as 0.05 percent, even in the developed world (World Economic Forum, Global Risk Report 2020). Hence, the data security of every configurational dimension of the Internet of Things (IoT) needs to pay attention towards reducing cyber-attacks on upcoming IoT-based systems across different sectors. To maintain confidentiality in regards to patient’s health and wellness records, may it be of a normal or a sportsperson, security, and privacy of their vital data are the most crucial dimensions to be managed inevitably.<\/p>\n

Can a threat intelligence model enable the integration of secure tunneling for the hash function to intelligently detect the anomaly and manage the breach at the earliest?<\/p>\n

The solution may seem difficult, but it is possible, as researchers (Dr Deepak Singh and his team) at Jaipuria Institute of Management<\/a>, Noida, believe. They are pinning their confidence on a multi-layered outlier detection model incorporating the unified threat management system for the Body Area Network (BAN) along with prosthetic sensors implanted in the patients (like heart valves) or performance aggregators for sportspersons may find an answer to it.<\/p>\n

The seamless confluence of a new class of computing architecture, known as Mist computing, with the body area network, enables outbound and inbound performance management of signals shared among the entire body area network for the patient. The IoT devices and sensors may get an extra layer of security for the environment without compromising signal comprehension in the healthcare process delivery. The intrusion divergence process for inbound security can protect the alien signals from breaching the secure environment for the body area network under surveillance.<\/p>\n

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The pandemic waves have brought forth the need for seamless transfer of vital medical data across geographies for an efficient medical care system for patients at risk.<\/p>\n

Sportspersons nowadays need to be smartly trained to remain fit and avert any adverse situations that hamper their performance. These prerequisites are an utter need to keep them equipped with bio-sensors to monitor all the required vitals during treatment and\/or training. The player may be wearing Near Field Communication (NFC) devices which could populate the server with vital signals from the body parts. Therefore, it calls for threat management, along with energy conservation, necessitating a model towards the usage of QI-powered multimodal architecture for the body area network.<\/p>\n

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, it is expected to keep the ecosystem safe for the user while delivering constant updates. The entire healthcare ecosystem may be designed for the personalized medication of a patient who is using a sophisticated lifesaving device like a prosthetic heart valve or an elderly person dependent on medical-aided ambulatory devices, or a sportsperson on a performance measurement system.<\/p>\n

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The full research paper can be accessed here Sharma, D., Singhal, T. K., & Singh, D. (2022). Threat Intelligence Model to Secure IoT-Based Body Area Network and Prosthetic Sensors. ECS Transactions, 107(1), 15417.\u00a0 \u00a0<\/em>DOI 10.1149\/10701.15417ecst<\/em><\/strong><\/p>\n

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