With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment.

For the application of hybrid tasks in priority-based preemptive systems, an innovative two-level hierarchical scheduling scheme is presented, and the time-triggered mode and event-triggered mode are adopted for the periodic real-time tasks and aperiodic real-time tasks respectively. First, the scheduling structure is proposed and the related concepts are defined. Then, the task model is established, and some crucial technologies such as scheduling algorithm, schedulability and jitter control are deeply researched. Subsequently, combined with Windows CE.NET, the experiment validation of scheduling scheme is carried out and the results demonstrate that the method can improve the determination of real-time tasks and reduce jitter. Specially, the jitter can be even eliminated if the task periods and tick-interval are rational. Finally, the choice of tick-interval is discussed. So the proposed scheduling scheme is efficient for hybrid tasks.

Current development method for computerized numerical control ( CNC ) system is mainly based on manual programming, so it is very time-consuming and is difficult to guarantee system performance. This paper presents a model-based development architecture, which is based on meat-model and model transformation, to address the problem. A domain-specific meta-model has been created to establish a special modeling environment for CNC system. In this modeling environment, different implementation models may be constructed according to different system requirements. These models can be translated into third-party simulation tool by model translators so that they can be analyzed and validated. Models interpreters are used to generate source code automatically from models to produce executable system. This development architecture can promote development efficiency, reduce development cost and improve system stability.