A Duo Autoencoder-SVM based Approach for Secure Performance Monitoring of Industrial Conveyor Belt System

•A deep learning technique for FDIA detection in presence of fault is proposed.•The sparse autoencoder is used for extracting the sparse features from raw inputs.•Threat detection threshold is fixed from normal input reconstruction residual.•The SVM is trained using generated residual for the FDIAs and fault classification.•Well suitable FDIA detection method free from system model and labelled sensor data. Process industries are fascinated by cyber-physical systems because of the potential to integrate physical systems and the cyber realm, resulting in efficient remote monitoring and control. The conveyor belt system has many critical parameters that require continuous attention, necessitating cyber-physical remote monitoring. Due to cloud-based monitoring of parameters, the system is vulnerable to cyber threats. The proposed technique combines a sparse autoencoder and support vector machine (SVM) to detect false data injection attacks (FDIAs) in the presence of sensor bias fault. The sparse autoencoder extracts sparse features and learns anomaly-free dynamics from the input sensor readings. Then, the trained SVM distinguishes attacks and fault by analysing reconstruction residuals of each measurement reading. The residuals also give an idea about the magnitude of abnormality. The proposed method's efficacy is evaluated in terms of accuracy, precision and false-alarm rate with the help of fault and FDIAs models.