The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21–25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions.

SnO₂ is a semiconducting metal oxide that is broadly employed as the active sensing material in chemiresitive gas sensors. Recent studies demonstrated the capability of SnO₂ sensors to detect various gases, including volatile organic compounds (VOCs), at room temperature. To explore the sensing mechanism, the adsorption of ethanol and acetone on the SnO₂ (110) and (101) surface facets is investigated via density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations. The role of surface oxygen defects, polarity of the VOC molecule, and pre-adsorbed oxygen species from the ambient atmosphere is explored. DFT results show that the direct adsorption of both ethanol and acetone on the (110) and the (101) surface facets is energetically favorable. The adsorption of both molecules is accompanied by the release of charge from the adsorbate gas to the surface, which promotes the sensing response. Binding strength of both molecules on the stoichiometric SnO₂ (110) and (101) surfaces is greater than that on the oxygen defective surfaces. Ethanol adsorption is generally stronger than acetone due to the bipolar nature of the hydroxyl (OH) group that interacts with the surface via two distinct charge transfer modes. Minimal interaction takes place between the pre-adsorbed oxygen species (O₂ ⁻) and the studied polar VOCs upon their adsorption on the reduced surfaces. Theoretical calculations are supplemented by sensing measurements for ethanol using SnO₂ nanostructured thin film sensors fabricated by an aerosol chemical vapor deposition (ACVD) technique. Taken together, these results suggest that the sensing mechanism of SnO₂ towards polar VOCs at room temperature can be explained by their direct adsorption on the surface rather than through their oxidation by means of ionosorbed oxygen species.

Smart water infrastructures are prone to cyber-physical attacks that can disrupt their operations or damage their assets. An algorithm was developed to identify suspicious behaviors in the different cyber-physical components of a smart water distribution system. The algorithm incorporated multiple modules of anomaly-detection techniques to recognize different types of anomalies in the real-time monitoring and control data. Trained artificial neural networks were used to detect unusual patterns that do not conform to normal operational behavior. Principal component analysis was conducted to decompose the high-dimensional space occupied by the sensory data to uncover global anomalies. The algorithm was trained using a historical data set of trusted observations and tested against a validation and a test data set, both featuring a group of simulated attack scenarios. The proposed approach successfully identified all the attacks featured in the Battle of the Attack Detection Algorithms (BATADAL) data sets with high sensitivity and specificity. Nevertheless, the performance was sensitive to high background noise in the sensory data.