This article presents a dataset produced to investigate how data and information quality estimations enable to detect aNomalies and malicious acts in cyber-physical systems. Data were acquired making use of a cyber-physical subsystem consisting of liquid containers for fuel or water, along with its automated control and data acquisition infrastructure. Described data consist of temporal series representing five operational scenarios – Normal, aNomalies, breakdown, sabotages, and cyber-attacks – corresponding to 15 different real situations. The dataset is publicly available in the .zip file published with the article, to investigate and compare faulty operation detection and characterization methods for cyber-physical systems.
Cyber-Physical Systems (CPS) are composed by multiple subsystems that encompass numerous interdependencies. Although indispensable and highly performant from a functional perspective, complex interconnectivity constitutes paradoxically a significant vulnerability when an anomaly occurs. Anomalies could propagate and impact the entire CPS with irreversible consequences. This paper presents an approach to assess the anomaly propagation impact risk on a three layers oriented graph which represents the physical, digital, and system variables of a CPS components and interdependencies. Anomalies are detected applying information quality measures, while potential propagation paths are assessed computing the cumulated risk represented by weights assigned to the graph edges. To verify the cascading impact of different anomalies four cyber-attacks -denial of service, sensor offset alteration, false data injection, and replay attack -were implemented on a simulated naval water distribution CPS. The propagation impact of three anomalies was successfully assessed and the corresponding estimated propagation path, if applicable, confirmed.
Sensor networks are becoming ubiquitous, enabling to improve decision-making and reducing human interaction by means of automatic or semi-automatic responses. However, due to deterioration or induced effects, sensors measures can be affected and produce anomalies that could alter decision-making. Most of the existing methods to identify sensors irregularities focus basically on detecting and discarding anomalous values, without looking for complementary information to understand generated anomalies. This paper presents an approach to obtain such complementary information by categorizing sensor anomalies, based on multidimensional quality assessment. It consists of two processing stages: an evaluation of data and information streams to estimate data quality imperfections and information quality dimensions; followed by the determination of agreement limits, compliant with normal states, to identify and categorize anomalies. The case study of discrete and analog sensors system installed in a simulator training platform of fuel tanks is presented, to illustrate an application of the proposed approach, considering 13 experimentally evaluated anomalies.
Abstract-In this paper, we present a platform we have designed and built in order to generate data and scenario traces that can serve as inputs and references when evaluating our algorithms for detecting cyber security intrusions. Our context is related to civilian and military ships and our research is performed within a strong partnership with industry and academy. As obtaining actual and accurate data sets are not straightforward, we have chosen do generate realistic datasets with a platform we have designed and built.
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