Recently, research on federated clustering has been actively studied to improve the performance of federated learning to solve the non-i.i.d issue. Federated clustering makes clusters with members who has similar characteristics of data which is used as inputs of federated learning, and each cluster trains an artificial intelligence model in a federated manner. However, if distances between members of a cluster configured through federated clustering is long in a network, the overhead related to federated learning becomes larger than expected and it may be lose the network cost benefits of federated learning. In this paper, we propose a DTW(Dynamic Time Warping) based federated clustering and MIP(Mixed Integer Programming)-based edge server placement in order to reduce the network overhead of federated learning caused by federated clustering under non-i.i.d setting.

Recently, with rising attention and widespread awareness of 5G technology, the rapid growth of mobile devices and various network infrastructures and services emerge. As means to provide responsive services and a guaranteed QoS level to individual demands while maintaining resource constraints, it is necessary to consider various factors affecting network service performance and dynamic network provisioning. In this paper, a Reinforcement Learning-based routing algorithm is proposed, which uses the information related to link stability to make routing decisions, called Reinforcement learning-based Routing with Link Stability (RRLS). To evaluate this algorithm, we applied the RRLS algorithm on a dynamic network provisioning framework and compared it to the RRLS algorithm and Dijkstra's algorithm. The result shows that the proposed algorithm performed better than Dijkstra's algorithm and shows that the proposed approach is an appealing solution for dynamic network provisioning routing.

Because of the activation of IoT (Internet of Things) devices due to the rapid development of recent communication technology, network traffic is currently fluctuating and increasing explosively. As existing network resource management policies are not sophisticated enough to cope with network conditions that change constantly, resource utilization can be lowered and costs can be higher. With the recent advances in deep learning techniques, network operators can manage networks intelligently. For the intelligent network, there is a technique which predict the state of network links. However, when the scale of the network increases, overall network management can be complicated. In addition, as the models of link state prediction are affected by the states of adjacent links, it is necessary to consider the spatio-temporal characteristics between links. In this paper, we propose a GCN(Graph Convolutional Neural Network)-GRU(Gated Recurrent Unit) based link state prediction technique. The proposed GCN-GRU model predicts network traffic by considering the spatio-temporal characteristics of each link state such as bandwidth, delay, and packet loss rate. Through extensive experiments on actual network traffic, the proposed GCN-GRU based link state prediction technique has shown to achieve 1.5% lower a mean absolute percentage error (MAPE) compared to a LSTM (Long Short term Memory) based link state prediction technique.

Recently, as the threat of Distributed Denial-of-Service attacks exploiting IoT devices has spread, source-side Denial-of-Service attack detection methods are being studied in order to quickly detect attacks and find their locations. Moreover, to mitigate the limitation of local view of source-side detection, a collaborative attack detection technique is required to share detection results on each source-side network. In this paper, a new collaborative source-side DDoS attack detection method is proposed for detecting DDoS attacks on multiple networks more correctly, by considering the detecting performance on different time zone. The results of individual attack detection on each network are weighted based on detection rate and false positive rate corresponding to the time zone of each network. By gathering the weighted detection results, the proposed method determines whether a DDoS attack happens. Through extensive evaluation with real network traffic data, it is confirmed that the proposed method reduces false positive rate by 35% while maintaining high detection rate.