The Game of Drones

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:

• Given the practical communications and energy constraints of these autonomous systems, they require their data acquisition to be tightly integrated with the sensing platform. In other words, instead of blindly collecting massive amounts of data, the data acquisition problem must address an active component of information acquisition, learning, and tracking.
• Given the practical hardware and energy constraints, the data analytics, inference, learning, and computations are required to be tightly integrated with the hardware (processors, memory architectures, etc). This integration of the hardware and the structure of the data into the design of the algorithms is an essential feature of these systems.
• Given the complexity of the design space and scalability requirements of the applications, the integration of data collection and analytics with the machine and sensor platforms must follow a design automation framework.

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:

• exploits: inject malicious code (e.g., Stuxnet, Jeep hack, and HVAC attack) and/or subverting the normal execution of a benign program (e.g., code-reuse attacks) and execute a malicious behaviour. For instance, a memory corruption flaw was found in the embedded webserver software used by over 200 different embedded device models.
• adversarial artificial intelligence: since more and more intelligence is built into embedded systems. Recent results on adversarial machine learning have shed light on a new and largely unexplored surface for malicious attacks jeopardizing the reliability of artificial intelligence model and machine learning models.

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.

Publications

• Bita Darvish Rouhani, Mohammad Samragh, Tara Javidi, and Farinaz Koushanfar. CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning. ArXiv preprint. September 2017.
• Ahmad-Reza Sadeghi, Matthias Schunter, Ahmad Ibrahim, Mauro Conti, Gregory Neven. SANA: Secure and Scalable Aggregate Network Attestation. In: 23rd ACM Conference on Computer and Communications Security (CCS) 2016. October 2016.
• Ahmad Ibrahim, Ahmad-Reza Sadeghi, Gene Tsudik, Shaza Zeitouni. DARPA: Device Attestation Resilient to Physical Attacks. In: ACM WiSEC 2016. July 2016.
• Nadarajah Asokan, Ferdinand Brasser, Ahmad Ibrahim, Ahmad-Reza Sadeghi, Matthias Schunter, Gene Tsudik, Christian Wachsmann. SEDA: Scalable Embedded Device Attestation. In: 22nd ACM Conference on Computer and Communications Security (CCS). October 2015.
• Ferdinand Brasser, Patrick Koeberl, Brahim El Mahjoub, Ahmad-Reza Sadeghi, Christian Wachsmann. TyTAN: Tiny Trust Anchor for Tiny Devices. In: 52nd Design Automation Conference (DAC) 2015. June 2015.
• Patrick Koeberl, Steffen Schulz, Ahmad-Reza Sadeghi, Vijay Varadharajan. TrustLite: A Security Architecture for Tiny Embedded Devices. In: European Conference on Computer Systems (EuroSys). April 2014.

Contacts