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学者姓名：舒兆港
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Abstract ：
Adversarial examples have been a significant research focus since their discovery. Recent studies have applied watermarking and data hiding techniques to generate meaningful adversarial perturbations that carry specific information, further enriching the functionality of adversarial examples. However, these methods struggle to balance time complexity with attack efficacy. To address this issue, we propose the Gradient Masking Method (GMM), introducing a new perspective on generating meaningful perturbations. Unlike previous techniques that directly embed watermarks or data as adversarial distortions, GMM embeds information into adversarial perturbations by selectively blocking the updating noise at specific positions using a message mask encoded from the information. The resulting perturbations represent the binary sequence of the embedded message. This method enables processed images to exhibit adversarial properties while simultaneously serving as carriers of information. Experimental results demonstrate the efficacy of our approach. In terms of computational cost, our method significantly outperforms previous techniques without compromising attack effectiveness. We evaluated the attack success rate of the proposed method across seven widely used classifier models, comparing it with baseline and black-box attack methods. Results confirm that our method performs effectively in common attack scenarios influenced by the message mask. The code of GMM can be found at https://github.com/Abin110/Gradient-Masking_.
Keyword ：
Adversarial attacks Adversarial attacks Deep neural networks Deep neural networks Meaningful adversarial example Meaningful adversarial example
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| GB/T 7714 | Lin, Hanbin , Liao, Wenxing , Shu, Zhaogang et al. GMM: Efficient information-containing adversarial perturbation based on gradient masking method [J]. | INFORMATION FUSION , 2026 , 127 . |
| MLA | Lin, Hanbin et al. "GMM: Efficient information-containing adversarial perturbation based on gradient masking method" . | INFORMATION FUSION 127 (2026) . |
| APA | Lin, Hanbin , Liao, Wenxing , Shu, Zhaogang , Liu, Xiaolong . GMM: Efficient information-containing adversarial perturbation based on gradient masking method . | INFORMATION FUSION , 2026 , 127 . |
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本发明公开了一种基于深度强化学习的服务功能链(SFC)动态调整方法，属于网络功能虚拟化(NFV)编排领域，所述基于深度强化学习的SFC动态调整方法包括：步骤1)构建面向数据中心的物理网络模型；步骤2)对数据中心所需要的SFC请求进行建模；步骤3)对SFC动态调整过程建模；步骤4)马尔可夫决策过程(MDP)建模；步骤5)搭建智能SFC动态调整算法(ISFCDAA)模型；步骤6)训练ISFCDAA模型；利用训练好的ISFCDAA进行SFC动态调整。本发明最终可实现数据中心网络中较高服务接受率和长期收益，为网络功能虚拟化编排器(Network Function Virtualization Orchestrator,NFVO)编排器的资源管理提供了有效的解决方案。
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| GB/T 7714 | 舒兆港 , 王媛滔 , 陈淑武 et al. 一种基于深度强化学习的服务功能链资源动态调整方法 : CN202411649701.8[P]. | 2024-11-19 . |
| MLA | 舒兆港 et al. "一种基于深度强化学习的服务功能链资源动态调整方法" : CN202411649701.8. | 2024-11-19 . |
| APA | 舒兆港 , 王媛滔 , 陈淑武 , 谢海辉 . 一种基于深度强化学习的服务功能链资源动态调整方法 : CN202411649701.8. | 2024-11-19 . |
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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. © 2025 by the authors.
Keyword ：
Big data Big data Blockchain Blockchain Data communication systems Data communication systems Denial-of-service attack Denial-of-service attack Digital storage Digital storage Distributed computer systems Distributed computer systems Information management Information management Internet of things Internet of things Learning algorithms Learning algorithms Learning systems Learning systems Network architecture Network architecture Network layers Network layers Network security Network security Network topology Network topology Software defined networking Software defined networking Traffic control Traffic control Unsupervised learning Unsupervised learning
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| GB/T 7714 | Peng, Shengmin , Tian, Jialin , Zheng, Xiangyu et al. DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN [J]. | Future Internet , 2025 , 17 (8) . |
| MLA | Peng, Shengmin et al. "DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN" . | Future Internet 17 . 8 (2025) . |
| APA | Peng, Shengmin , Tian, Jialin , Zheng, Xiangyu , Chen, Shuwu , Shu, Zhaogang . DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN . | Future Internet , 2025 , 17 (8) . |
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With the continuous development of Network Function Virtualization (NFV) technology, Virtual Network Function (VNF) migration has become a crucial approach to optimizing network resource utilization, reducing service latency, and improving service quality. However, in dynamic network environments, VNF migration faces challenges such as resource overload, service request prioritization, migration cost optimization, routing overhead, and energy consumption. To address these challenges, this paper proposes a priority-aware and multi-objective optimization-based VNF migration algorithm, namely the Lagrangian Fish Optimization for VNF Migration (LFO-VNM) Algorithm. This algorithm integrates the Lagrangian relaxation method with the Artificial Fish Swarm Algorithm (AFSA) to dynamically adjust resource allocation and migration paths, optimizing migration cost, network performance, and node energy consumption while prioritizing high-priority service requests. First, a Mixed-Integer Linear Programming (MILP) model is established to quantify the impact of VNF migration on network link load, node resource consumption, and service performance. Based on this, a multi-objective optimization model is formulated, considering network bandwidth, latency, migration cost, and energy consumption. This model is decomposed into a series of linear subproblems, which are more efficiently solved using the Lagrangian relaxation method. Finally, leveraging the global search capability of AFSA, an efficient solution algorithm, LFO-VNM, is designed to optimize VNF migration decisions. Experimental results demonstrate that the proposed algorithm not only improves computational efficiency but also effectively reduces total cost and energy consumption, outperforming existing migration algorithms across various network topologies. This study provides an effective solution for VNF migration and resource scheduling in complex network environments.
Keyword ：
Multi-objective optimization Multi-objective optimization Network Function Virtualization Network Function Virtualization Priority awareness Priority awareness VNF migration VNF migration
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| GB/T 7714 | Luo, Yu , Shu, Zhaogang , Chen, Shuwu et al. Multi-objective optimization algorithm for VNF migration with priority awareness in dynamic networks [J]. | COMPUTER NETWORKS , 2025 , 272 . |
| MLA | Luo, Yu et al. "Multi-objective optimization algorithm for VNF migration with priority awareness in dynamic networks" . | COMPUTER NETWORKS 272 (2025) . |
| APA | Luo, Yu , Shu, Zhaogang , Chen, Shuwu , Tu, Qiang , Wu, Xianzhang , Lin, Qingjie . Multi-objective optimization algorithm for VNF migration with priority awareness in dynamic networks . | COMPUTER NETWORKS , 2025 , 272 . |
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This paper addresses the problem of mapping, scheduling, and routing of virtual network functions (VNF) on a service function chain (SFC) that is sensitive to latency in a virtual network. A scheduling algorithm for VNF is proposed, which aims to minimize the SFC rejection rate while taking into account VNF mapping, scheduling, and traffic routing during the scheduling process. To achieve this goal, a Markov decision process (MDP)-based VNF scheduling model is established that guarantees SFC resource requirements are met. The model uses the D3QN (Dueling Double DQN) algorithm based on composite rules to select the SFC at each scheduling time point, and selects virtual nodes and routes using a routing optimization algorithm to minimize the SFC rejection rate. We compare our algorithm with the single rule, DQN and genetic algorithm, and the simulation results show that the proposed algorithm can reduce the rejection rate of SFC by approximately 8% compared to genetic algorithms.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Delay-aware Delay-aware Service function chain Service function chain Virtual network functions Virtual network functions VNF scheduling VNF scheduling
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| GB/T 7714 | Liu, Zhiwei , Shu, Zhaogang , Chen, Shuwu et al. Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning [J]. | COMPUTER NETWORKS , 2024 , 246 . |
| MLA | Liu, Zhiwei et al. "Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning" . | COMPUTER NETWORKS 246 (2024) . |
| APA | Liu, Zhiwei , Shu, Zhaogang , Chen, Shuwu , Zhong, Yiwen , Lin, Jiaxiang . Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning . | COMPUTER NETWORKS , 2024 , 246 . |
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The introduction of Network Function Virtualization (NFV) and Software-Defined Network (SDN) architectures has significantly reduced the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of network system. However, NFV orchestration management also brings about challenges. After the initial deployment of VNFs, due to the volatility of network requests, the original deployment may not be able to meet user resource demands. The key issue is how to readjust resources dynamically to accommodate more network requests without violating Quality of Service (QoS) for users. Several existing techniques can be used to achieve this goal, such as horizontal scaling, vertical scaling, and virtual network function (VNF) migration. However, these techniques inevitably incur some overhead, such as the cost of instantiating VNF and link rerouting. Additionally, resource adjustment may also result in unbalanced distribution of network resources. In this paper, an Intelligent Service Function Chain Dynamic Adjustment Algorithm (ISFCDAA) is proposed to address the above challenges. Firstly, an Integer Linear Programming (ILP) model is established with the objective of minimizing the long-term adjustment cost and reducing the imbalance of resource distribution. Then we transform the optimization process into a Markov Decision Process (MDP). Secondly, to solve the problems that the state and action space is too large and the state transition probability is uncertain in MDP, a SFC dynamic adjustment algorithm based on deep reinforcement learning is proposed. This algorithm can obtain an approximate optimal adjustment strategy for ILP model. The simulation results show that ISFCDAA can reduce the adjustment overhead and maintain a balanced distribution of network resources while ensuring the QoS. Compared with the existing algorithms, the average standard deviation of resource distribution of ISFCDAA is reduced by up to 9.90%, the average acceptance rate of ISFCDAA is improved by up to 39.57%, and the average long-term profit is improved by up to 42.92%. The incorporation of cost and demand-sensitive considerations into ISFCDAA enhances its responsiveness to fluctuating network demands, solidifying its effectiveness in dynamic resource management scenarios.
Keyword ：
Network function virtualization orchestrator Network function virtualization orchestrator Resource management Resource management Service function chain Service function chain
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| GB/T 7714 | Wang, Yuantao , Shu, Zhaogang , Chen, Shuwu et al. A cost and demand sensitive adjustment algorithm for service function chain in data center network [J]. | COMPUTER NETWORKS , 2024 , 242 . |
| MLA | Wang, Yuantao et al. "A cost and demand sensitive adjustment algorithm for service function chain in data center network" . | COMPUTER NETWORKS 242 (2024) . |
| APA | Wang, Yuantao , Shu, Zhaogang , Chen, Shuwu , Lin, Jiaxiang , Zhang, Zhenchang . A cost and demand sensitive adjustment algorithm for service function chain in data center network . | COMPUTER NETWORKS , 2024 , 242 . |
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针对在虚拟网络中如何对延迟敏感的服务功能链(SFC)上的虚拟网络功能(VNF)在调度过程时将VNF映射、调度联合解决并最小化VNF处理延迟的问题，提出了一种基于强化学习自调节鲸鱼优化算法，通过不同环境自动调节鲸鱼优化算法中的参数，同时使用交叉、变异以及领域算子整体来提高鲸鱼优化算法性能，在保证满足SFC的约束需求的情况下，以最小化完成时间为目标，建立VNF调度模型。模型通过改进鲸鱼优化算法和强化学习自动调节参数来加快收敛速度，找寻最优解。仿真结果表明，所提出的算法与HGWOA算法和GABL算法相比在性能上分别提升10%与16%。
Keyword ：
VNF调度 VNF调度 服务功能链 服务功能链 网络功能虚拟化 网络功能虚拟化 鲸鱼优化算法 鲸鱼优化算法
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| GB/T 7714 | 刘智伟 , 舒兆港 . 基于强化学习与鲸鱼优化算法的低延迟网络功能调度方法 [J]. | 信息技术与信息化 , 2024 , 8 (05) : 23-30 . |
| MLA | 刘智伟 et al. "基于强化学习与鲸鱼优化算法的低延迟网络功能调度方法" . | 信息技术与信息化 8 . 05 (2024) : 23-30 . |
| APA | 刘智伟 , 舒兆港 . 基于强化学习与鲸鱼优化算法的低延迟网络功能调度方法 . | 信息技术与信息化 , 2024 , 8 (05) , 23-30 . |
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本发明公开了一种基于SPI RAM的多CPU异步通信系统及方法，系统包括主CPU、多个从CPU以及SPI RAM模组；所述主CPU以及多个从CPU均与所述SPI RAM模组连接；所述主CPU，用于在将原始数据存储在SPI RAM模组后通知多个从CPU；所述多个从CPU，用于在接收到所述主CPU的通知后，从SPI RAM模组中读取所述原始数据，并将所述原始数据转换成文件格式保存到内部的FLASH存储芯片。本发明基于单/多路SPI RAM的异步数据交换方案，可以提高多CPU架构的通信效率，保证各个CPU执行时间可以放到更为紧急的任务上，另外通过SPI总线对比双端口RAM的并口大大减少了IO的使用数量。从而有效降低了CPU的采购成本，可以选用低主频、低成本的CPU来实现相应的功能。
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| GB/T 7714 | 陈淑武 , 董欣运 , 吴建煌 et al. 一种基于SPI RAM的多CPU异步通信系统及方法 : CN202311549081.6[P]. | 2023-11-20 . |
| MLA | 陈淑武 et al. "一种基于SPI RAM的多CPU异步通信系统及方法" : CN202311549081.6. | 2023-11-20 . |
| APA | 陈淑武 , 董欣运 , 吴建煌 , 刘礼慧 , 舒兆港 . 一种基于SPI RAM的多CPU异步通信系统及方法 : CN202311549081.6. | 2023-11-20 . |
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本发明提供了三相交流电压输入和输出信号实时同步过零翻转电路及系统，涉及交流电压过零点检测技术领域，通过实际测试，实现了输入端电压信号，输出端方波的同步监测，经过滤波翻转，两次滤波移相位，做到了输入端信号和输出端方波在信号处理上的实时同步，不存在相位上的误差，前端输入和后端输出同步翻转，真正的实现了在处理方波时就是对应的此刻的交流电压信号，在交流电控制领域具有重要的意义。
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| GB/T 7714 | 陈淑武 , 李俊朋 , 舒兆港 . 三相交流电压输入和输出信号实时同步过零翻转电路及系统 : CN202410580592.2[P]. | 2024-05-11 . |
| MLA | 陈淑武 et al. "三相交流电压输入和输出信号实时同步过零翻转电路及系统" : CN202410580592.2. | 2024-05-11 . |
| APA | 陈淑武 , 李俊朋 , 舒兆港 . 三相交流电压输入和输出信号实时同步过零翻转电路及系统 : CN202410580592.2. | 2024-05-11 . |
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With the development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) has emerged as a novel network architecture in today's Internet era. It can separate the control plane from the data plane, allowing rapid packet forwarding in the Internet through a centralized controller. However, SDN environments are vulnerable to traditional Distributed Denial of Service (DDoS) attacks. This paper proposes a new dual layer strategy to try to mitigate the question. First, by using blockchain technology and smart contract in the northbound interface to store the flow tables required for SDN networks, security is increased. Then, we use the Token Bucket algorithm and Time Window algorithm to build the first-tier strategy to defend against obvious DDoS attacks. To detect unobvious DDoS attacks, we design the second-tier strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm to perform binary classification on data, thereby identifying abnormal traffic. We use the currently publicly available DDoS dataset CIC-DDoS2019 for experimental verification. The results show that using this strategy in SDN networks results in an average deviation of data Round-Rip Time (RTT) approximately 38.86% lower than in the original SDN networks without this strategy. Additionally, the accuracy of DDoS attack identification reaches 91.29%. This means that with the implementation of this strategy, DDoS attacks can be effectively identified without compromising the stability of data transmission in SDN network environments.
Keyword ：
Blockchain Blockchain Distributed Denial of Service Distributed Denial of Service Machine learning Machine learning Software Defined Networks Software Defined Networks
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| GB/T 7714 | Tian, Jialin , Shu, Zhaogang , Chen, Shuwu et al. Enhanced DDoS Defense in SDN: Double-Layered Strategy with Blockchain Integration [J]. | 2024 13TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CIRCUITS AND SYSTEMS, ICCCAS 2024 , 2024 : 380-384 . |
| MLA | Tian, Jialin et al. "Enhanced DDoS Defense in SDN: Double-Layered Strategy with Blockchain Integration" . | 2024 13TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CIRCUITS AND SYSTEMS, ICCCAS 2024 (2024) : 380-384 . |
| APA | Tian, Jialin , Shu, Zhaogang , Chen, Shuwu , Xie, Haihui , Liu, Xiaolong , Qiu, Caiyu . Enhanced DDoS Defense in SDN: Double-Layered Strategy with Blockchain Integration . | 2024 13TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CIRCUITS AND SYSTEMS, ICCCAS 2024 , 2024 , 380-384 . |
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