Abstract Software-Defined Networking (SDN), as a new network architecture, has brought convenience, but also suffered from the threat of Distributed Denial of Service (DDoS) attack. However, most existing DDoS attack detection schemes for SDN employ only a single detection method, leading to imbalances in detection speed, system overhead, and detection accuracy. Even though a few schemes improve detection efficiency and accuracy through two-stage detection, they still suffer from low system flexibility and do not support dynamic threshold adjustment. In order to resolve these issues, we propose an adaptive two-stage DDoS attack detection scheme with Dynamic Threshold Adjustment (ATS-DTA for short), which contains three sub-modules. More specifically, by dividing DDoS attack detection into two modules: a conditional entropy-based network traffic anomaly detection phase and a DDoS attack detection phase based on machine learning methods. Additionally, an adaptive threshold adjustment module is introduced to improve the system’s flexibility. Finally, the experimental results show that our scheme, compared to related schemes, not only significantly improves detection accuracy and speed but also supports flexible and dynamic threshold adjustment. Specifically, our method achieves an average accuracy improvement of 1.91% and a precision increase of 1.23% over baseline methods, underscoring its effectiveness in adapting to complex and evolving network environments. These advantages illustrate that our ATS-DTA scheme provides a more balanced, efficient, and reliable solution for DDoS detection in dynamic network scenarios.

Service workflows are fundamental in software ser-vice systems where standardized processes are implemented as workflow models to achieve automated service execution. Despite the obvious benefits of model-driven architecture, its mainstream adoption is hampered by the need for extensive knowledge and sophisticated modeling abilities to create such models. Recently, multi-agent frameworks have thrived in handling complex tasks by harnessing collective intelligence to tackle intricate problems. MASFlow, a progressive multi-agent collaboration framework for automated service workflow model generation, is presented in this paper. Through the use of specialized agents that represent various team responsibilities, MASFlow replicates real-world design cooperation by breaking down the modeling process into three coordinated phases: Structuring, Orchestration, and Re-view. Experimental results demonstrate that MASFlow effectively mitigates the hallucination generation phenomenon commonly observed in large language models (LLMs) when handling complex service workflows through a phased task decomposition strategy. With an accuracy rate of 92.83%, the generated service workflow model demonstrated notable advantages above existing mainstream neural network architecture techniques and solutions that directly use LLMs.

The increasing production of service description documents by enterprises and service providers has prompted the need for automated service workflow generation. This paper explores a method that combines large language models (LLMs) and graph neural network (GNNs) to address this challenge. Automatically extracting service workflows from documents also provides a convenient and efficient solution for situations where process mining algorithms cannot be utilized due to the absence of logs. The proposed method begins by employing LLMs to extract activity nodes and conditional nodes from service workflow description documents. These nodes are then organized into an initial graph using chain connections and auxiliary connections provided by the LLMs. Subsequently, an inductive GNN is utilized to analyze node embeddings within the service workflow, enabling the learning of semantic representations and connection rules. This facilitates the discovery of potential connections among nodes, leading to the generation of efficient service workflows. The experimental results demonstrate that the proposed method achieves an F1-Score of 0.818 in predicting node relationships. Furthermore, ablation experiments have been conducted to validate the effectiveness of incorporating both LLMs and GNNs.

An accurate and efficient trust evaluation mechanism is the cornerstone of maintaining the stability of SDN networks, especially in face of the escalating threats like Distributed Denial of Service (DDoS) attacks. The traditional trust evaluation mechanisms in SDN often lack of adaptability and accuracy, due to that they typically rely on the direct trust or ignore the influence of indirect and historical trust factors which lead to inaccurate trust evaluation and lower efficiency. In order to solve these problems, we propose a novel multidimensional trust evaluation mechanism consisting of three sub-modules: Device Behavior Trust Evaluation Scheme (DBTES), Machine Learning-based Trust Evaluation Scheme (MLTES), and Bayesian-Based Trust Evaluation Scheme (BBTES). These modules work together to enhance the precision and adaptability of trust assessments by capturing various aspects of device behavior. Additionally, we introduce a trust fusion algorithm that combines the Analytical Hierarchy Process(AHP) with Criteria Importance Through Inter-criteria Correlation (CRITIC) to optimize weight distribution, further improving the accuracy and robustness of trust evaluations. Our approach overcomes the limitations of existing methods by providing a more comprehensive and adaptable trust assessment. Simulation results show that under varying Malicious Device Ratio conditions, the MDTM method improves the task success rate by up to 4.39% compared to traditional methods, along with an increase in average trust values by 5.16%. Additionally, with changing historical factors, MDTM further improves trust values by 5.33%. These results demonstrate the enhanced resilience and effectiveness of our approach in maintaining network stability and security within SDN environments.

Abstract In recent years, the rapid development of the Internet of Things (IoT) technology has provided a strong technical support for the technological transformation of the logistics industry. The informatization development of logistics industry increasingly relies on the Internet of Things technology represented by Radio Frequency Identification (RFID) technology. These technologies lead the whole business process to optimize the business process in the direction of accurate, efficient and real‐time. In order to solve the problem that the reader cannot identify the label information correctly due to the phenomenon of data collision in the application of RFID technology, this paper proposes an anti‐collision algorithm based on Slotted Random Regressive‐style Binary Search Tree (SR‐RBST). Based on Slotted ALOHA (SA) the method proposed in this paper uses the Regressive‐style Binary Search Tree (RBST) to process the RFID labels in the collision time slot. With the same size of tags, the SR‐RBST algorithm needs less total time slot and has higher efficiency and shorter identification time, while with the increase of the number of tags, the SR‐RBST anti‐collision algorithm has more obvious advantages. The SR‐RBST algorithm effectively improves the time slot utilization efficiency of the system.