Resilient and Secure Drone Swarms
We are increasingly surrounded by interconnected embedded systems, which collect sensitive information, and perform safety-critical operations.
Most embedded systems perform simple tasks upon reception of a command, in a predefined manner. However, in recent years, embedded systems have been increasingly designed to carry out autonomous collaborative tasks.
Networks of autonomous embedded systems, such as, vehicular ad-hoc networks, robotic factory workers, search/rescue robots, and drones, are already being used for performing urgent, tiresome, and critical tasks with minimal human intervention.
For example, drones are (envisioned to be) used for various tasks, such as search and rescue, construction site management, security and surveillance, cargo delivery, and natural disasters prediction and warning.
The autonomous operation of autonomous systems over light-weight and constrained embedded devices requires a rethinking of data acquisition and computational tasks. More specifically, such systems must be designed with the efficiency of the data collection and processing in mind. The main challenges are:
The interconnection of such systems poses a formidable security challenge, as it increases their susceptibility to attacks, and magnifies the consequences of the success of such attacks.
Attacks against autonomous and collaborating systems can be launched:
The goal of this project is to design collaborative autonomous systems that are resilient to classical exploits as well as adversarial artificial intelligence mentioned above.
To reach our goals there are a number of challenges to tackle. Our work is currently focusing on the following:
Ahmad-Reza Sadeghi, Technische Universität Darmstadt.
Ahmad's expertise includes trusted and secure computing and security and privacy for Internet of Things.
Farinaz Koushanfar, University of California San Diego (UCSD).
Farinaz's expertise includes data analytics in constrained settings, embedded systems security, and robust machine learning.
Tara Javidi, University of California San Diego (UCSD).
Tara's expertise includes statistical analysis and robust machine learning.