AI For Real-Time Embedded Applications: Honeypot Learning and Predicting for OoD Attack Patterns

Today’s automobile is more than a mechanical tool; it contains a myriad of computers, sensors, IoT, and embedded nodes. The embedded system is the heart of a vehicle’s electronic system because of its versatility and flexibility. Furthermore, these systems are becoming increasingly sophisticated and interconnected, both to each other and to the Internet. However, with great convenience also comes great concerns about the security and privacy of our digital assets. Unfortunately, the security implications of this complexity and connectivity have mostly been overlooked, even though ignoring security could have disastrous consequences.

The attacks on embedded systems are evolving and becoming more complex and destructive. In this project, we develop a security incident response system for embedded systems designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in computational power, memory, and energy overhead. Furthermore, this project’s overcoming will help Thales identify the problem and associated cyber threat risks and pave the way for more efficient hardware/software solutions.

Faculty Supervisor:

Abdellah Chehri

Student:

Partner:

Thales Recherche et Technologie

Discipline:

Computer science

Sector:

Artificial Intelligence; Information and Communications Technology; Technology

University:

Royal Military College of Canada

Program: