Identificação de Nós Maliciosos em Redes Complexas Baseada em Visões Locais

Many social, biological and information systems can be described through complex network models. Complex networks display common structural features, such as the smallworld and scale-free properties. However, nodes in these networks may not cooperate with each other, presenting a selfish behavior to preserve their resources. Furthermore, the presence of malicious nodes can damage the network operation, as they may attack the network in several different ways, like inserting, modifying or eliminating information in the network. Trust evaluation algorithms are a useful incentive for encouraging selfish nodes to collaborate and for isolating malicious ones. Nodes which refrain from cooperation or present a malicious behavior get a lower trust value and may be penalized as other nodes tend to cooperate only with highly trusted ones. This paper presents an algorithm to calculate the number of malicious and/or selfish nodes in a network based on local trust views that each node has about their neighbors. The algorithm points out to the network manager exactly which nodes they are. Simulation results over four real complex networks demonstrate the effectiveness of the proposed approach. In fact, it presents an error margin smaller than 15% for up to 35000 malicious or selfish nodes in networks of 70000 nodes. If the number of malicious nodes goes under 5000 for the same networks, the error margin is around one node.