Event-Driven Distributed Trust Model for Internet of Things Network
A vision of the future Internet has been introduced in which various computing devices will be connected to each other to form a network called the Internet of Things. The Internet of Things facilitates human life by providing many applications and smart devices that can be controlled remotely. IoT security is a challenging task due to the inherent characteristics of IoT, especially the heterogeneity of nodes in terms of resources. Trust management, by calculating and analyzing the trust between nodes, enables the node to make an appropriate and reliable decision in communication between nodes. The goal of trust management schemes in a distributed system is to predict the future behavior of nodes based on their previous behavior. In this paper, an event-based distributed trust management method is proposed that calculates the trust between objects using weighted summation. In this method, nodes can evaluate the behavior of other nodes. According to the performed simulation, the proposed method is faster in comparison with the DDTMS method and identifies malicious nodes in fewer transactions and is also more resistant to on-off and bad-mouthing attacks.
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Using Long Short-Term Memory and Convolutional neural networks to Detect Distributed Denial of Service Attacks
S.Mojtaba Matinkhah *, Ali Khakbaz, Fazlolah Adibnia
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