Software-defined networking (SDN) is a new networking paradigm that realizes the fast management and optimal configuration of network resources by decoupling control logic and forwarding functions. However, centralized network architecture brings new security problems, and denial-of-service (DoS) attacks are among the most critical threats. Due to the lack of an effective message-verification mechanism in SDN, attackers can easily launch a DoS attack by faking the source address information. This paper presents DoSGuard, an efficient and protocol-independent defense framework for SDN networks to detect and mitigate such attacks. DoSGuard is a lightweight extension module on SDN controllers that mainly consists of three key components: a monitor, a detector, and a mitigator. The monitor maintains the information between the switches and the hosts for anomaly detection. The detector utilizes OpenFlow message and flow features to detect the attack. The mitigator protects networks by filtering malicious packets. We implement a prototype of DoSGuard in the floodlight controller and evaluate its effectiveness in a simulation environment. Experimental results show the DoSGuard achieves 98.72% detecion precision, and the average CPU utilization of the controller is only around 8%. The results demonstrate that DoSGuard can effectively mitigate DoS attacks against SDN with limited overhead.

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Software-defined networking (SDN) decouples the control plane and data plane through OpenFlow technology and allows flexible network control. It has been widely applied in different areas and has become a focus of attention in the future network. With SDN’s development, its security problem has become a necessary point of research to be solved urgently. In this paper, we propose a novel attack, namely, the packet injection exploiting attack. By maliciously injecting false hosts into SDN network topology, attackers can further use them to launch a denial of service (DoS) attack. The consequences affect the throughput and processing capabilities of the controller, severely consume data plane resources, and ultimately affect the entire network. To prevent the packet-injection exploiting attack, we designed PIEDefender, an efficient, protocol-independent component built on SDN controllers to detect and mitigate attacks effectively. We implement the PIEDefender prototype on the Floodlight controller and assess the effectiveness in the software environment. Experimental results show that PIEDefender achieves a 97.8% injection detection precision and a 97.96% DoS detection precision, incurring an average CPU consumption of 10%. The evaluation demonstrates that the PIEDefender can effectively mitigate the attack against SDN with limited overhead.

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In the field of video action classification, existing network frameworks often only use video frames as input. When the object involved in the action does not appear in a prominent position in the video frame, the network cannot accurately classify it. We introduce a new neural network structure that uses sound to assist in processing such tasks. The original sound wave is converted into sound texture as the input of the network. Furthermore, in order to use the rich modal information (images and sound) in the video, we designed and used a two-stream frame. In this work, we assume that sound data can be used to solve motion recognition tasks. To demonstrate this, we designed a neural network based on sound texture to perform video action classification tasks. Then, we fuse this network with a deep neural network that uses continuous video frames to construct a two-stream network, which is called A-IN. Finally, in the kinetics dataset, we use our proposed A-IN to compare with the image-only network. The experimental results show that the recognition accuracy of the two-stream neural network model with uesed sound data features is increased by 7.6% compared with the network using video frames. This proves that the rational use of the rich information in the video can improve the classification effect.