3. Network Model and Attack Model

3.1. Network Model

In this paper, we consider a scenario in which all the sensor nodes are randomly deployed in a two-dimensional space. Nodes are neither added nor removed from network after deployment. It assumes that sensor nodes have the same capabilities of computing, communicating, and storing, initial energy level, and communication range, only when the two nodes enter the communication range of each other to start communication. Based on these assumptions, WSNs can be abstracted as a graph G = (V, E), where V is the set of all nodes and E is the set of all edges. Each edge e(i, j) \in E denotes that the two nodes are located within each other's transmission range. Each node keeps a list of neighboring nodes which stores their unique ID, communication information, and trust relationship. It assumes that neither the source nor the destination is malicious. Malicious nodes do not collude themselves and all communication links are bidirectional. And the communication channel is secure.


3.2. Attack Model for WSNs

With the open and remote deployment environment, WSNs are generally susceptible to various internal attacks, including black hole attack, wormhole attack, Sybil attack, and grey-hole attack. The attack behavior of those malicious nodes shows diversity, such as discarding routing packets, injecting large amounts of redundant information and error information, maliciously modifying data packets, and providing unreal recommendation trusted data information. According to the attack behavior and target of malicious attack, we divide the various attacks into three categories: attacks on routing protocols in WSNs, attacks on communications data or messages, and attacks on trust models in WSNs. The target of the first kind of malicious attack is the routing protocol. The malicious attack behavior of this type discards all the routing packets or drops part of the routing packets, which makes the data packets unable to be forwarded properly between nodes. This type of attack includes black hole attack, grey-hole attack, and wormhole attack. Due to the vulnerability of the wireless communication channel, the second type of malicious attack is that the malicious nodes can easily capture transmitting data information through a wireless link. The target of this type of malicious attack is communications data or messages. The transmitting data can be easily conducted with eavesdropping, forgery, and tamper. This type of attack includes Dos attack and message tampering attack. The third type of malicious attack is a special kind of attack, whose target is the trust management. This type of malicious attack can destroy the trust model by providing false information. This type of attack includes on-off attack, conflicting behavior, selfish attack, and bad-mouthing attack. As we know, trust management system can deal with most of the existing attacks and improve the security of the network. However, it is difficult to detect these malicious nodes completely by conventional trust model. The proposed DTEM can evaluate trustworthiness of sensor nodes more precisely and identify the different malicious nodes more effectively.