A static analysis based tool to detect vulnerabilities in android automotive applications

With millions of Android device activation per day and billions of application
installations from Google Play, Android is becoming one of the most widely
used operating systems, even in automotives. Based on the Linux kernel,
Android operating system is open and
exible enough to run on dierent devices
having varied hardware conguration. Modern smart car infotainment
systems allow users to connect an Android device to the vehicle. The device
then interacts with the hardware of the car, hence providing new interaction
mechanisms to the driver. However this can be misused and become a major
security breach into the car with subsequent security concerns : the Android
device can both read sensitive data (speed, model, airbag status) and send
dangerous commands (brake, lock, airbag explosion). Moreover, this scenario
is unsettling since Android devices connect to the cloud, opening the door
to remote attacks by malicious users of the cyberspace. The applications are
bringing potential vulnerability when they access private information from
the resources of the devices. In this project, I have developed a static analysis
based tool to detect vulnerabilities in Android Automotive Applications.
It mainly focuses on the presence of malicious content in the application by
analysing permissions, API calls, etc. The acquired data is then classied
using machine learning techniques.

MSC CS 2017-2019 INT Dr Tony Thomas