*2.5. Cyber-Security in Water Systems*

One paper regarded the very interesting topic of the state of the art of cyber-security in water systems [15]. It is clear that also in water systems the evolution from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks, artificial intelligence, and IoT devices will increase significantly the risk of cyber-attacks. The authors highlighted the importance of protecting water infrastructure from malicious entities that can conduct industrial espionage and sabotage against these systems. The review of [15] focused on the aspects of the system vulnerability, of the actual measures, and the perspective to improve the cyber-security of water systems. The authors found that the majority of cyber-security studies were carried out on drinking water systems, others on drinking water treatment systems, and only a few on nondrinking water systems (i.e., canal automation systems used for irrigation and wastewater systems). However, while the impacts of cyber-physical attacks are increasingly discussed in the literature, only few studies address the problem of how to efficiently protect micro components in smart water systems. Therefore, it was concluded that further works should specifically focus on making smart water systems reliable and safe. To successfully enable smart water systems in practice, future research should focus on efficiently protecting micro components by including cyber-physical components in the resilience assessment of urban water systems.

Finally, the last two papers hosted in the Special Issue were not fully aligned to the topic of water distribution networks, but they are very interesting in the more general paradigm of smart networks and big data collection with innovative smart sensors.

The first paper proposed the usefulness of hydrological time-series water depth clustering that can be extended to other smart measures. Specifically, clustering of recorded information is a meaningful statistical method to gain knowledge out of a multitude of real-time measured data. For urban drainage systems, where an increasing number of sensors are installed, this information might also be of great interest for the detection and forecasting of flooding events. The researchers [16] investigated how data-driven unsupervised machine learning algorithms can be used to group hydraulic-hydrological data of measurements in storm water drainage systems. By investigating different clustering and performance evaluation methods, suggestions are given about what kind of method should be applied according to the type of detection events (e.g., short-duration or long duration). This can be implemented as a flood early warning system.

Although not aligned to the topic of water distribution network, the last paper on IoT for wastewater treatment plants also provided useful suggestions to the technical and scientific community about the application of wireless sensor networks that can be a promising approach for different fields of urban water management. In [17] was presented a low-cost IoT system for water quality monitoring for wastewater treatment plants at a close-to-market stage. With a novel ion chromatography detection method, they integrated and tested a nitrate and nitrite analyzer under real conditions. The results of comparing laboratory and low-cost IoT systems revealed the reliability of the proposed device.
