## 4 Attack Model
Eventually, data from one part of the WSN to another are transmitted through the attacker nodes, while the data transmitted over the legitimate communication channel are discarded. The attacker nodes can eavesdrop or modify information, compromising data confidentiality and integrity. In addition, in such cases, the routing in WSNs is disrupted. The goal of the sinkhole attack is to redirect all traffic in WSN through a single node controlled by the attacker. In addition, Figure 4b shows in blue that the attacker compromises one of the nodes in the WSN and eliminates all routes in the network, leaving only one route through the controlled node. This attack can also be realized through adding a new node to the  
Figure 4: Case study of air pollution monitoring example (in an urban infrastructure). **(a)** Initial WSN indicating the connection between the nodes; **(b)** WSN under the influence of wormhole and sinkhole attacks. WSN and result in compromising data confidentiality and integrity as well as disrupting the routing in the network. In addition, the controlled node in the network can be given any role, and an attacker can interfere with the implementation of that role. In the works mentioned above, their authors mostly consider the simplest (i.e., common) versions of these attacks, that is, variants of the attacks without taking into account the property of decentralization of the network. However, self-organization and, to a greater extent, decentralization may allow an attacker to mount an attack less noticeable to security means than in its common variation. An example of a more covert variation of the attack is an exploitation of decentralization and role distribution in order to illegitimately obtain a specific role to learn any extra information from other network nodes. Another example is to exploit the role redistribution mechanism to mount a Denial-of-Service (DoS) attack maliciously, namely a flood attack, wasting the available energy resources of the nodes. All this allows us to conclude that the analysis of the unauthorized influences of self-organization and decentralization properties on the attacks described above is highly relevant and effective and, hence, it is an important task. Therefore, this implies the need to develop algorithms for the detection of such attacks as crucial steps to counter these influences. As stated above, the considered attacks on self-organizing decentralized WSNs can be divided into several subclasses, characterizing the object they influence. Namely, these can be attacks on data transmitted over the network; impacts on node software; influences on physical part devices of WSN nodes; and impacts on the interaction protocol and communication channel. Summarizing for each class of attacks, possible types of attacks are given in Table 4.  
Note that the attacks given in Table 2 are feasible both in common WSNs and in self-organizing decentralized WSNs. However, after a WSN becomes one with properties of self-organization and decentralization, the attacker finds expanded opportunities to perform these attacks by using actions that are a priori considered as legitimate ones in such networks. For example, in a common WSN with a fixed structure, the appearance of a new node in the network usually presents a clear feature of a malicious modification of the network structure, while in a self-organizing network, this fact, in itself, cannot directly indicate any attack. Therefore, to detect attacks on self-organizing decentralized WSNs, an expanded set of informational features and more complex methods for their detection are required. For each of the attacks considered in the work, we analyzed to what extent the properties of self-organization and decentralization, as well as the role-based functioning of the network, can influence the feasibility of the attacks.