# H2O: Secure Interactions in IoT via Behavioral Fingerprinting
## 3 General Model
If these objects are of public use in the environment, they will be denoted as \(N_{LPU}\), whereas, if they belong to a \(PAN\), they will be denoted as \(N_{LPAN}\). These devices have low computational resources and are not suitable to work with machine learning solutions. Some examples are intelligent thermostats, remotely controllable household equipment, and weather-based automated lawn irrigation systems. Figure 1 shows a graphical representation of our scenario with the above subgroups involved. In particular, two users, namely [PERSON] and [PERSON] and their PANs, are depicted. Figure 1: A representation different nodes’ groups in H2O Network. Smart objects, even belonging to the different groups, can communicate with each other. Transactions can be performed to share data or to require measures/services offered by target smart objects. Figure 2 shows a graphical representation of possible communications in our scenario deployed in a smart office. The main idea underlying our approach is that an object in the H2O network, when getting in touch with another object, can compare its behavior by exploiting a behavioral model [53]. Through this comparison, it can state if the object it wants to communicate with is reliable or not. From the IoT scientific literature, we know that objects can have three different levels of sensing capabilities, namely: user awareness, ambient awareness, and social awareness [54]. In our context, the definition of such capabilities can be declined as follows:  
* _Owner awareness_ refers to the smart object ability to understand personal contexts and behavioral patterns (e.g., human mobility, human activity, preferences, etc.) referred to its owner. Observe that, for the \(N_{PPU}\) and \(N_{LPU}\) groups, we can use the term _Human awareness_ instead of _Owner awareness_, because these objects can be used by more than one person in the network. * _Environment awareness_ concerns the capability of smart objects to derive information of a particular environment through their equipment (e.g., temperature or humidity if the object is a sensor, etc.). * _Social awareness_ represents the ability of smart objects to participate and exchange information with communities. All these sensing capabilities can be translated to measurements and properties. Starting from them, an object profile, including a behavioral fingerprint, can be built. A fingerprint is a complex model considering different dynamics related to the object activities, such as: (_i_) how the object interacts with the other objects in the network, (_ii_) how the owner of the object interacts with it and (_iii_) the value of the metrics about the environment that the object can compute. As stated in the Introduction, our approach leverages behavioral fingerprinting as a mean to build a mechanism to improve communication reliability in an IoT. Therefore, the first step concerns the construction of object profiles including such behavioral fingerprints. Due to the fully distributed nature of our application context, these models are built by objects with high computational capabilities, on the basis of interaction data among pair of nodes. To do so, we define a _safe starting phase_, in which all the interactions are considered safe and no malicious node is involved in the environment. During this phase, each node  
Figure 2: A representation of H2O Network with entity operations. is in charge of _(i)_ acquiring interaction data with its peers and _(ii)_ proceeding with the computation of the object profile and the behavioral fingerprint. In general, only nodes belonging to \(N_{PPU}\) or \(N_{PPAN}\) can build complex models; however, delegation strategies can be adopted to extend the approach also to nodes belonging to \(N_{LPPI}\) or \(N_{LPAN}\). The details about the behavioral fingerprinting for H2O objects are described in Section 4. After the _safe starting phase_, the network moves to a fully operational mode. During this second phase, network participants can leverage behavioral models trained in the previous phase.