Data Fusion-Based Anomaly Detection for Networked Critical Infrastructures

The dramatic increase in the use of Information and Communication Technologies (ICT) within Networked Critical Infrastructures (NCIs), e.g. the power grid, has lead to more efficient and flexible installations as well as new services and features, e.g. remote monitoring and control. Nevertheless, this has not only exposed NCIs to typical ICT systems attacks, but also to a new breed of cyber-physical attacks. To alleviate these issues, in this paper we propose a novel approach for detecting cyber-physical anomalies in NCIs using the concept of cyber-physical data fusion. By employing Dempster-Shafer's "Theory of Evidence" we combine knowledge from the cyber and physical dimension of NCIs in order to achieve an Anomaly Detection System (ADS) capable to detect even small disturbances that are not detected by traditional approaches. The proposed ADS is validated in a scenario assessing the consequences of Distributed Denial of Service (DDoS) attacks on Multi Protocol Label Switching (MPLS) Virtual Private Networks (VPNs) and the propagation of such disturbances to the operation of a simulated power grid.JRC.E.2-Technology Innovation in Securit

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process