Deep Learning Based Security Solutions for the Internet of Medical Things

Speaker:  Heena Rathore – Austin, TX, United States
Topic(s):  Security and Privacy

Abstract

Intelligent healthcare has gained importance in the recent past since it allows continuous, remote monitoring of patients away from hospitals and doctors. With advances in technology, doctors now can improve quality of medical service for their patients through a surgical methodology that includes implantable embedded medical devices. Addition of connectivity to such devices is the key enabling technology. Devices are now connected to each other and to the world wide web (internet), which leads to the use of the term Internet of Medical Things. To enable this, medical devices now have WiFi/Cellular chips on them so that they can talk to each other, in addition to the traditional roles of sensing and actuating. However, on the other end, addition of connectivity and computing platform now makes these devices more prone to hacking. 

This talk focuses on how deep learning techniques can be utilized to make these devices secure. This talk covers different problems of security ranging between internal to communication attacks such as authentication, classification, prediction on implantable medical devices such as insulin pump implants, deep brain stimulators and cardiac defibrillator. Thi talk will cover efficient techniques, such as multi-layer perceptron neural networks, recurrent neural networks, etc. to overcome these problems. This talk also discusses how these algorithms can be implemented on the node or on the edge to enable real-time decision making.

After this lecture, audience will be able to apply deep learning techniques to make medical devices secure from unauthorized access. They will learn the tools to compare different types of techniques and also a working understanding of how to implement such algorithms on embedded processors.

About this Lecture

Number of Slides:  50-55
Duration:  50 minutes
Languages Available:  English
Last Updated: 

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