Biblio

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

Due to the wide implementation of communication networks, industrial control systems are vulnerable to malicious attacks, which could cause potentially devastating results. Adversaries launch integrity attacks by injecting false data into systems to create fake events or cover up the plan of damaging the systems. In addition, the complexity and nonlinearity of control systems make it more difficult to detect attacks and defense it. Therefore, a novel security situation awareness framework based on particle filtering, which has good ability in estimating state for nonlinear systems, is proposed to provide an accuracy understanding of system situation. First, a system state estimation based on particle filtering is presented to estimate nodes state. Then, a voting scheme is introduced into hazard situation detection to identify the malicious nodes and a local estimator is constructed to estimate the actual system state by removing the identified malicious nodes. Finally, based on the estimated actual state, the actual measurements of the compromised nodes are predicted by using the situation prediction algorithm. At the end of this paper, a simulation of a continuous stirred tank is conducted to verify the efficiency of the proposed framework and algorithms.