schliffen/Information-fusion-for-intrusion-detection

intended to detect anomaly cyber attacks that targeting controlled dynamical systems by fusing the data received from multiple resources. A detector is proposed for fused dynamic system involving uncertainty and noise to recognise anomaly activities. Two models of fusion in data-level and state-level is proposed and error dynamics is extracted for both fusion methods. Information fusion is implemented via Kalman filtering in state dynamic as well as error dynamics

MATLAB