Distributed Rule Anomaly Detection in SDN-based IoT: Towards a Comprehensive Approach

The rapid increase in the number of equipment connected to different networks in the world has led to the development of diverse and new applications in the Internet of Things, which often use the current network infrastructure. In other words, force the network administrator to implement complex network policies manually. Due to this significant growth of equipment and the increase in the complexity of traditional network configuration, software-defined networks (SDN) integrate and facilitate network management by separating the control and data layers from each other and creating network rules in the data layer. For these facilities, these networks appear to be a good infrastructure for IoT networks, which will enable network programming to develop new and more efficient services to meet real needs. In addition, the variety of IoT equipment can increase complex and inconsistent network rules in SDN-based switches, making network management difficult. Accordingly, in this paper, we will try to model the behavior of anomaly rules distributed in software-defined networks such as FTD, FBF, and irrelevant anomalies that have been created by different apps in the Internet of Things. It can identify their relationship with other rules in the network and avoid registering them.