Abstract Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors.

A crucial requirement for Software Defined Network (SDN) is that data plane forwarding behaviors should always agree with control plane policies. Such requirement cannot be met when there areforwarding anomalies, where packets deviate from the paths specified by the controller. Most anomaly detection methods for SDN install dedicated rules to collect statistics of each flow, and check whether the statistics conform to the “flow conservation principle”. We find these methods have a limited detection scope: they look at one flow each time, thus can only check a small number of flows simultaneously. In addition, dedicated rules for statistics collection can impose a large overhead on flow tables of SDN switches. To this end, this paper presents FOCES, a network-wide forwarding anomaly detection and localization method in SDN. Different from previous methods, FOCES applies a new kind of flow conservation principle at network wide, and can check forwarding behaviors ofallflows in the network simultaneously, without installing any dedicated rules. Finally, FOCES applies a voting-based method to localize malicious switches when anomalies are detected. Experiments with four network topologies show that FOCES can achieve a detection precision higher than 90%, when the packet loss rate is no larger than 10%, and a localization accuracy of around 80% when the packet loss rate is no larger than 5%.

A crucial requirement for Software Defined Network (SDN) is that data plane forwarding behaviors should always agree with control plane policies. Such requirement cannot be met when there are forwarding anomalies, where packets deviate from the paths specified by the controller. Most anomaly detection methods for SDN install dedicated rules to collect statistics of each flow, and check whether the statistics conform to the flow conservation principle. Such per-flow detection methods have a limited detection scope: they look at one flow each time, thus can only check a limited number of flows simultaneously. In addition, dedicated rules for statistics collection can impose a large overhead on flow tables of SDN switches. To this end, this paper presents FOCES, a network-wide forwarding anomaly detection method in SDN. Different from previous methods, FOCES applies a new kind of flow conservation principle at network wide, and can check forwarding behaviors of all flows in the network simultaneously, without installing any dedicated rules. Experiments show FOCES can achieve a detection precision higher than 90% for four network topologies, even when packet loss rates are as high as 10%.