Geetha Thamilarasu's page
Mobile Embedded and Wireless Security Research @UWB

Our research group focuses on various aspects of Mobile, Embedded, & Wireless Security in the domains of wireless body area networks, cyber-physical systems, Internet of Things and mobile devices. We also conduct extensive research in wireless and mobile health systems and applications, including designing novel secure mhealth apps for health care. We also focus on using machine learning in designing and developing anomaly based detection systems in these emerging systems.

Current Projects

Secure Framework for Mobile Health Applications

Wireless and mobile health systems represent the evolution of m-health systems from traditional telemedicine platforms to wireless and mobile configurations. Given the highly sensitive nature of personal health data transmitted and because of perceived risk to users' health, secure and trustworthy communication in these systems is of utmost importance. Currently there are nearly 100,000 mobile health and fitness related apps in the market. While a majority of them are fitness related, health related mobile apps are increasingly being used to store, relay and analyze medical and personal health data. There is however a severe dearth of security mechanisms provided for any of the apps currently used in healthcare. The goal of this research is to develop a secure platform for mobile health applications. Specifically, our goal is to provide a multi-level security solution that focuses on authenticating the identity of the user, encrypting the data stored on the mobile device, encrypting the data that is transmitted as well as securing the cloud system often used to retrieve and analyze the data collected from the application. The secure mobile framework is currently developed for Android platform but will be extended to include other mobile platforms in future.

Securing Wearables, Wireless and Mobile medical devices

In addition to mobile health applications, our research also investigate security in both wearable and implantable wireless medical devices. Malwares, unauthorized access of a medical device unauthorized change of settings of these devices such as insulin pumps or a cardiac pacemaker can potentially be life threatening to the patients. As power is a very limited resource in these implantable medical devices, our goal is to develop non-intrusive and non-cryptographic malware detection solutions.

Security in Internet of Things

In technology, when you think of being connected, we often think in terms of communication via computers, tablets and smartphones. "Internet of Things" (IoT), one of the buzzwords in the current technology world is changing our perception of digital connectivity. IoT is basically a networking technology where 'things' such as everyday physical objects (e.g. home appliances, gadgets, wearables), places and environments are interconnected with each other and to the Internet to create a huge information system. Beyond smart homes (think of Apple's homekit and Google's nestlabs home automation), IoT will have a huge impact in a number of other applications including smart city, smart grid, connected cars, connected health to name a few. With 50 billion of connected devices anticipated by the year 2020, security risks and threats pertaining to IoT are growing and changing rapidly. More connected devices mean more possibilities for hackers to target. As soon as you connect something to the internet then it's hackable and it's a target. For example, researchers have already shown that cars can be hacked, the brakes, steering, transmission etc. can be easily taken control of and ultimately be crashed. The task of securing these pervasive IoT environments is becoming complex and challenging. My research group is currently evaluating the various security concerns in IoT environment and designing a secure platform independent solution to secure and defend the IoT systems. Particularly, we apply novel machine learning techniques to improve the accuracy and reliability of our detection algorithms.

Current Students

  • Nicholas Handaja
  • Nhu Ly
  • Darshan Patel
  • Niharika Jain

    Former Students

  • Allison Gibson (NBC Security)
  • Illestar Wu (Microsoft)
  • Saransh Sharma (Amazon)
  • Medha Srivastava (Google)
  • Erin Beckwith (Lockheed Martin)
  • William Schneble (NBC Security)
  • Julio Perez
  • Jeremy Woods (Google)
  • Shiven Chawla (GE Healthcare Security)
  • Sida Gao (Facebook Security)
  • Adedayo Odesile
  • Christopher Lakin (Rhino Security Labs)
  • Chris Luong
  • Ngoc Luu
  • Pavel Krivopustov