# Water-Tight IoT-Just Add Security
## 3 Threat Landscape  
Motivations for attackers to attack digital solutions exist, independently of their level of integration with water utilities, and this justifies the need to ensure they are secure as well [16; 17]. As presented in the previous section, digital solutions can be complex and might interact with several external actors to collect data, access services or to simply provide their own service to their users. Attackers thus have a wide choice of attack vectors when targeting a digital solution, such as IoT devices, applications, third-party services, etc. Building on the Risk Identification Database (RIDB) which gathers the generic risk events associated with the implementation of the digital solution of DWC by the cities, one can derive a classification of the attacks to the digital solutions in DWC. This classification is presented in Figure 2, and groups the attacks in six different classes which sum up the different types of attacks that can relate to a digital solution:  
Figure 1: Generic architecture diagram for a digital solution in DWC. **Attacks on IoT sensors:**: As already explained above, IoT sensors are for many solutions a keystone as they constitute the source of data. Sensors are particularly vulnerable since they are often deployed in the wild (i.e., accessible by almost anyone), and are difficult and costly to secure (and thus have historically had poor security). While the data of the sensors themselves might not be very valuable to an attacker, being able to manipulate these data to trigger incorrect outputs by the services could have severe consequences. In addition, IoT sensors have in the past already been (and continue to be) compromised at scale to be integrated in botnets [18]. **Attacks on infrastructure:**: Attacking the infrastructure is a way for the attackers to either disrupt the services (by, for instance, launching Denial of Services (DoS) attacks on it) or to gain unauthorized access to resources. This could also be a way for attackers to later use their access to a service to attack a water utility using and trusting this service. **Attacks on ML/AI:**: Compared to the other types of attack in this classification, attacks against machine learning (ML) and artificial intelligence (AI) are less known and appear to be fairly new. Attackers can for instance use specially crafted inputs to mislead the algorithms. Famous examples of such attacks include, for instance, cars being tricked into speeding by placing tape on speed signs. Other attacks consist of an attacker feeding incorrect data to the classifier (also known as data poisoning), polluting the model in such way that its own data are later classified as good data or, on the contrary, so that good data are in fact classified as incorrect. Finally, models have an intrinsic value, and an attacker might want to steal them. **Attacks on applications:**: Applications (web, mobile, API, etc.) are usually exposed and, if compromised, can lead to data leaks, unauthorized access to resources and actions or allow for data manipulation and denial of service. **Human errors/failures:**: While not being an attack per se, human error can lead to the same consequences. If a user is given access to data or actions he/she should not have access  
Figure 2: Classification of the attacks against DWC digital solutions. to, he/she could misuse it (intentionally or not) and effectively create a situation similar to an attack (for instance, a user could be given access to an alert system and trigger an alarm, leading operators to take decisions based on misleading data). **Social engineering:**: Like human errors, a user could be tricked by a malicious person into performing harmful actions, potentially leading to dangerous consequences as well. When securing a digital solution, it is important to take into consideration who and what one is defending against.