1- Learning-based vulnerability analysis of cyber-physical systems

This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS, where the low-level control is based on a feedback controller and an observer (e.g., the extended Kalman filter (EKF)), while also employing an anomaly detector. We then introduce two methods for generating effective stealthy attacks, based on feed-forward neural networks (FNN) and recurrent neural networks (RNN). Both types of attack-generator models are trained offline, using a cost function that combines the attack impact on the estimation error (and thus control) and the residual signal used for anomaly detection; this enables the trained models to recursively generate effective yet stealthy sensor attacks in real-time while requiring different levels of system information at runtime. We evaluate the impact of these stealthy adversarial attacks on autonomous vehicle case study in CARLA environement.

Unattacked scenario

FNN attack

RNN attack

2- Stealthy perception-based attacks on unmanned aerial vehicles

In this project, we study vulnerability of unmanned aerial vehicles (UAVs) to stealthy attacks on perception-based control. To guide our analysis, we consider two specific missions: (i) ground vehicle tracking (GVT), and (ii) vertical take-off and landing (VTOL) of a quadcopter on a moving ground vehicle. Specifically, we introduce a method to consistently attack both the sensors measurements and camera images over time, in order to cause control performance degradation (e.g., by failing the mission) while remaining stealthy (i.e., undetected by the deployed anomaly detector). Unlike existing attacks that mainly rely on vulnerability of deep neural networks to small input perturbations (e.g., by adding small patches and/or noise to the images), we show that stealthy yet effective attacks can be designed by changing images of the ground vehicle's landing markers as well as suitably falsifying sensing data. We illustrate the effectiveness of our attacks in Gazebo 3D robotics simulator.

Ground vehicle tracking (GVT) without attack

Vertical take-off and landing (VTOL) without attack

Ground vehicle tracking (GVT) in the
presence of attack

Vertical take-off and landing (VTOL) in the
presence of attack