  Research evidence in Cyber-Physical Systems (CPS) shows that the introduced
tight coupling of information technology with physical sensing and actuation
leads to more vulnerability and security weaknesses. But, the traditional
security protection mechanisms of CPS focus on data encryption while neglecting
the sensors which are vulnerable to attacks in the physical domain.
Accordingly, researchers attach utmost importance to the problem of state
estimation in the presence of sensor attacks. In this work, we present SecSens,
a novel approach for secure nonlinear state estimation in the presence of
modeling and measurement noise. SecSens consists of two independent algorithms,
namely, SecEKF and SecOPT, which are based on Extended Kalman Filter and
Maximum Likelihood Estimation, respectively. We adopt a holistic approach to
introduce security awareness among state estimation algorithms without
requiring specialized hardware, or cryptographic techniques. We apply SecSens
to securely localize and time synchronize networked mobile devices. SecSens
provides good performance at run-time several order of magnitude faster than
the state of art solutions under the presence of powerful attacks. Our
algorithms are evaluated on a testbed with static nodes and a mobile quadrotor
all equipped with commercial ultra-wide band wireless devices.
