Software Defined Networking (SDN) centrally manages the network data layer to improve the programmability and flexibility of networks by the controller. Because of centralized control, SDN is vulnerable to Distributed Denial of Service (DDoS) attacks. In order to protect the security of SDN, a method based on K-means++ and Fast K-Nearest Neighbors (K-FKNN) is proposed for DDoS detection in SDN, and the modular detection system is presented in the controller. The detailed experiments are conducted to evaluate the system performance. The results of the experiments show that K-FKNN improves the detection accuracy and efficiency of K-Nearest Neighbors (KNN), and has high precision and stability of DDoS detection in SDN. INDEX TERMS Software defined networks, network security, DDoS detection, K-nearest neighbors.

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Software-defined networking (SDN) emerges as an innovative network paradigm, which separates the control plane from the data plane to improve the network programmability and flexibility. It is widely applied in the Internet of Things (IoT). However, SDN is vulnerable to DDoS attacks, which can cause network disasters. In order to protect SDN security, a DDoS detection method using cloud-edge collaboration based on Entropy-Measuring Self-organizing Maps and KD-tree (EMSOM-KD) is designed for SDN. Entropy measurement is utilized to select the ideal SOM map and classify SOM neurons considering the limitation of dead and suspicious neurons. EMSOM can detect most flows directly and filter out a few doubtable flows. Then these flows are fine-grained, identified by KD-tree. Due to the limited and precious resources of the controller, parameter computation is performed in the cloud. The edge controller implements DDoS detection by EMSOM-KD. The experiments are conducted to evaluate the performance of the proposed method. The results show that EMSOM-KD has better detection accuracy; moreover, it improves the KD-tree detection efficiency.

A knowledge graph is a special kind of graph data, which consists of a triad. Each node in the knowledge graph has several attributes and their attribute values. The storage of the knowledge graph has been the object of academic research, and in this paper, we conduct an in-depth study on the knowledge graph data indexing and compression storage algorithm supported by the RDF graph model, and propose an optimization algorithm for the storage query after the second-level compression. The core of this paper is that after the second-level compression of the k2-tree tree, the submatrices are prioritized in terms of the size of data blocks, and when retrieving data, they are retrieved according to the priority, so that the blocks in front are both subject and object at the same time, which can improve the efficiency of data reading, so that the parts with more information will be retrieved first, instead of the traditional sequential retrieval, which tends to retrieve the null values or the data with less information.

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