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学者姓名：刘延华
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Abstract ：
对电力网络鲁棒性进行评估与预测,有利于网络管理人员感知网络系统运行现状,及时采取措施应对可能的风险.为此提出了一种基于改进鲸鱼优化算法的电力调度数据网鲁棒性预测模型.首先,构建了电力调度数据网鲁棒性指标体系,并采用字段提取及公式映射等方法,实现了面向指标体系的数据降维处理;此外,进一步研究了基于混沌映射与自适应权重的WOA-BP改进算法(CA-WOA-BP),实现了电力网络鲁棒性预测方法.实验结果表明,与WOA-BP算法相比,所提出的改进算法加快了预测模型的收敛速度,并克服了陷入局部最优的情况,同时将预测值误差百分比降低了5.3%,有助于用户更准确及时地感知电力调度数据网系统鲁棒性的态势.
Keyword ：
混沌映射 混沌映射 电力调度数据网 电力调度数据网 网络鲁棒性预测 网络鲁棒性预测 自适应权重 自适应权重 鲸鱼优化算法 鲸鱼优化算法
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| GB/T 7714 | 陈斌 , 李泽科 , 余斯航 et al. 基于CA-WOA-BP算法的调度数据网鲁棒性预测 [J]. | 南方电网技术 , 2025 , 19 (2) : 10-18 . |
| MLA | 陈斌 et al. "基于CA-WOA-BP算法的调度数据网鲁棒性预测" . | 南方电网技术 19 . 2 (2025) : 10-18 . |
| APA | 陈斌 , 李泽科 , 余斯航 , 郭久煜 , 林碧海 , 刘延华 . 基于CA-WOA-BP算法的调度数据网鲁棒性预测 . | 南方电网技术 , 2025 , 19 (2) , 10-18 . |
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Network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\%$$\end{document} and 7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7\%$$\end{document} increase in precision on weighted averages.
Keyword ：
Attack dimension Attack dimension Ensemble learning Ensemble learning Feature fusion Feature fusion Network intrusion detection Network intrusion detection
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| GB/T 7714 | Sun, Xiaolong , Gu, Zhengyao , Zhang, Hao et al. Network intrusion detection based on feature fusion of attack dimension [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (6) . |
| MLA | Sun, Xiaolong et al. "Network intrusion detection based on feature fusion of attack dimension" . | JOURNAL OF SUPERCOMPUTING 81 . 6 (2025) . |
| APA | Sun, Xiaolong , Gu, Zhengyao , Zhang, Hao , Gu, Jason , Liu, Yanhua , Dong, Chen et al. Network intrusion detection based on feature fusion of attack dimension . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (6) . |
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User trajectories are denser and highly dynamic in mobile crowdsensing (MCS) system, rendering traditional privacy budget allocation schemes insufficient. Additionally, the protection of semantic location privacy is often neglected in these schemes, making them vulnerable to inference attacks. To address these deficiencies, a user trajectory privacy protection strategy based on deep reinforcement learning is proposed in this article. First, a differential privacy-based user trajectory privacy protection algorithm (DP-upps) is designed to protect the privacy by perturbing the extracted trajectory feature points. Then, a deep reinforcement learning-based privacy budget allocation algorithm (DRL-pbas) is introduced. The privacy budget is dynamically adjusted by deep reinforcement learning option to continuously adapt to environmental changes and maximize benefits. After that, a DRL-pbas based user privacy protection strategy (DRL-UPPS) is proposed, integrating semantic location privacy protection. This approach combines the previous two algorithms, allowing the privacy budget to be allocated in a way that effectively balances the protection of physical and semantic location privacy and data quality. Ultimately, a large number of simulation experiments are conducted based on real datasets. The experiments demonstrate that DRL-UPPS can effectively balance privacy protection and data quality, resisting the privacy attacks. Compared with other strategies, DRL-UPPS improves comprehensive privacy protection capability by approximately 10% and data utility by approximately 8%.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Deep reinforcement learning (DRL) Deep reinforcement learning (DRL) differential privacy differential privacy Differential privacy Differential privacy mobile crowdsensing mobile crowdsensing Perturbation methods Perturbation methods Privacy Privacy Protection Protection Resource management Resource management semantic location semantic location Semantics Semantics Sensors Sensors Servers Servers Trajectory Trajectory trajectory privacy protection trajectory privacy protection
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| GB/T 7714 | He, Ding , Zhang, Jing , Xu, Li et al. DRL-UPPS: User Trajectory Privacy Protection Strategy Based on Deep Reinforcement Learning in Mobile Crowdsensing [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2025 . |
| MLA | He, Ding et al. "DRL-UPPS: User Trajectory Privacy Protection Strategy Based on Deep Reinforcement Learning in Mobile Crowdsensing" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2025) . |
| APA | He, Ding , Zhang, Jing , Xu, Li , Liu, Yanhua , Ye, Xiucai . DRL-UPPS: User Trajectory Privacy Protection Strategy Based on Deep Reinforcement Learning in Mobile Crowdsensing . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2025 . |
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Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within a blockchain-based smart healthcare system, existing efforts employ benefit distribution and reputation assessment methods to enhance performance. Yet, there remains a significant gap in multidimensional assessment strategies and consensus improvements in addressing complex healthcare scenarios. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, thus effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and credibly records the assessment results on the blockchain. Then, we propose an improved PBFT algorithm, grounded in the reputation assessment, to augment blockchain consensus efficiency. Finally, BtRaI designs a token-based reward and punishment mechanism to motivate honest participation in the blockchain, inhibit potential misbehavior, and promote enhanced service quality in the healthcare system. Theoretical analysis and simulation experiments conducted across various scenarios demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node fault tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 s in a 100-node consortium chain. BtRaI's reputation assessment and token incentive mechanism, characterized by realistic differentiation granularity and change curves, are well-suited for dynamic and complex healthcare service environments.
Keyword ：
Blockchain Blockchain Consensus mechanism Consensus mechanism Healthcare Internet of Things Healthcare Internet of Things Incentive mechanism Incentive mechanism PBFT algorithm PBFT algorithm Reputation assessment Reputation assessment
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| GB/T 7714 | Liu, Yanhua , Liu, Zhihuang , Zhang, Qiu et al. Blockchain and trusted reputation assessment-based incentive mechanism for healthcare services [J]. | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2024 , 154 : 59-71 . |
| MLA | Liu, Yanhua et al. "Blockchain and trusted reputation assessment-based incentive mechanism for healthcare services" . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 154 (2024) : 59-71 . |
| APA | Liu, Yanhua , Liu, Zhihuang , Zhang, Qiu , Su, Jinshu , Cai, Zhiping , Li, Xiaoyan . Blockchain and trusted reputation assessment-based incentive mechanism for healthcare services . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2024 , 154 , 59-71 . |
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Cloud-assisted electronic health record (EHR) sharing plays an important role in modern healthcare systems but faces threats of distrust and non-traceability. The advent of blockchain offers an attractive solution to overcome this issue. Many efforts are devoted to promoting secure, flexible, and multi-featured blockchain-based EHR sharing. Yet, the problem of seeking out suitable healthcare providers and communicating information beyond the EHR has unfortunately been ignored. In this paper, we propose SeCoSe, a novel EHR sharing scheme to address these concerns. SeCoSe enables patients and their general practitioners to autonomously seek out and stay in touch with their preferred healthcare professionals. Specifically, a searchable and repeatable transformation identity-based encryption (SRTIBE) is proposed to achieve dynamic and flexible authorization updates. Moreover, we design attribute-identity mapping contracts and evidence-based contracts on the blockchain to enable on-demand retrieval of anonymous identities and ensure tamper resistance and traceability of system transactions. Furthermore, we employ the advanced messages on-chain protocol (AMOP) to facilitate the online communication of off-chain messages. Detailed security analysis and extensive evaluations demonstrate that SeCoSe is privacy-secure, traceable, and attack-resistant. SeCoSe has lower overhead for repeated authorization and transformation, on-chain transactions can be responded to within seconds, and online communication can handle the transmission of 49,000 messages in about 6 seconds.
Keyword ：
blockchain blockchain Electronic health records Electronic health records healthcare service seeking healthcare service seeking identity-based encryption identity-based encryption smart contract smart contract
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| GB/T 7714 | Liu, Zhihuang , Hu, Ling , Cai, Zhiping et al. SeCoSe: Toward Searchable and Communicable Healthcare Service Seeking in Flexible and Secure EHR Sharing [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 4999-5014 . |
| MLA | Liu, Zhihuang et al. "SeCoSe: Toward Searchable and Communicable Healthcare Service Seeking in Flexible and Secure EHR Sharing" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 4999-5014 . |
| APA | Liu, Zhihuang , Hu, Ling , Cai, Zhiping , Liu, Ximeng , Liu, Yanhua . SeCoSe: Toward Searchable and Communicable Healthcare Service Seeking in Flexible and Secure EHR Sharing . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 4999-5014 . |
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为分析黑灰产网络资产图谱数据中黑灰产团伙掌握的网络资产及其关联关系,提出一种基于图挖掘的黑灰产运作模式可视分析方法.首先,在网络资产图谱数据中锁定潜在团伙线索;其次,根据潜在线索、黑灰产业务规则挖掘由同一黑灰产团伙掌握的网络资产子图,并识别子图中的核心资产与关键链路;最后,基于标记核心资产和关键链路的黑灰产子图实现可视分析系统,从而直观发现黑灰产团伙掌握的网络资产及其关联关系,帮助分析人员制定黑灰产网络资产打击策略.经实验验证,该方法能有效、直观地分析和发现黑灰产团伙及其网络资产关联关系,为更好监测黑灰产网络运作态势提供必要的技术支持.
Keyword ：
关键链路 关键链路 可视分析 可视分析 子图挖掘 子图挖掘 网络资产 网络资产 黑灰产 黑灰产
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| GB/T 7714 | 尚思佳 , 陈晓淇 , 林靖淞 et al. 基于图挖掘的黑灰产运作模式可视分析 [J]. | 信息安全研究 , 2024 , 10 (1) : 48-54 . |
| MLA | 尚思佳 et al. "基于图挖掘的黑灰产运作模式可视分析" . | 信息安全研究 10 . 1 (2024) : 48-54 . |
| APA | 尚思佳 , 陈晓淇 , 林靖淞 , 林睫菲 , 李臻 , 刘延华 . 基于图挖掘的黑灰产运作模式可视分析 . | 信息安全研究 , 2024 , 10 (1) , 48-54 . |
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As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network's generalization capabilities. Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic. Compared with the benchmark methods, our method reaches 99.9% on low-rate DDoS (LDDoS), flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%, 31% and 8%.
Keyword ：
DDoS DDoS DNN DNN improved generalized entropy improved generalized entropy real-time real-time
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| GB/T 7714 | Liu, Yanhua , Han, Yuting , Chen, Hui et al. IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN [J]. | CMC-COMPUTERS MATERIALS & CONTINUA , 2024 , 80 (2) . |
| MLA | Liu, Yanhua et al. "IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN" . | CMC-COMPUTERS MATERIALS & CONTINUA 80 . 2 (2024) . |
| APA | Liu, Yanhua , Han, Yuting , Chen, Hui , Zhao, Baokang , Wang, Xiaofeng , Liu, Ximeng . IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN . | CMC-COMPUTERS MATERIALS & CONTINUA , 2024 , 80 (2) . |
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With the rapid development of Industry 4.0, the importance of cyber security for industrial control systems has become increasingly prominent. The complexity and diversity of industrial control systems result in data with high dimensionality and strong correlation, posing significant challenges in obtaining labeled data. However, current intrusion detection methods often demand large amounts of labeled data for effective training. To address this limitation, this paper proposes a semi-supervised anomaly detection framework, called SFSD, which leverages feature selection and deviation networks to detect anomalies in industrial control systems. Specifically, we introduce a feature selection algorithm (IG-PCA) that utilizes information gain and principal component analysis to reduce the dimensionality of features in industrial control data by eliminating redundant features. Then, we propose a semi-supervised learning method based on an improved deviation network, which utilizes an anomaly scoring network to learn end-to-end anomaly scores for the training data, thus assigning anomaly scores to each training data. Finally, using a limited amount of anomaly-labeled data, we design a specific deviation loss function to optimize the anomaly scoring network, enabling a significant score bias between positive and negative samples. Experimental results demonstrate that the proposed SFSD outperforms existing semi-supervised anomaly detection frameworks by improving the accuracy and detection rate by an average of 1-2%. Moreover, SFSD requires less training time compared to existing frameworks, resulting in a training time reduction of approximately 10% or more.
Keyword ：
Feature selection Feature selection Industrial control systems Industrial control systems Intrusion detection Intrusion detection PCA PCA Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Liu, Yanhua , Deng, Wentao , Liu, Zhihuang et al. Semi-supervised attack detection in industrial control systems with deviation networks and feature selection [J]. | JOURNAL OF SUPERCOMPUTING , 2024 , 80 (10) : 14600-14621 . |
| MLA | Liu, Yanhua et al. "Semi-supervised attack detection in industrial control systems with deviation networks and feature selection" . | JOURNAL OF SUPERCOMPUTING 80 . 10 (2024) : 14600-14621 . |
| APA | Liu, Yanhua , Deng, Wentao , Liu, Zhihuang , Zeng, Fanhao . Semi-supervised attack detection in industrial control systems with deviation networks and feature selection . | JOURNAL OF SUPERCOMPUTING , 2024 , 80 (10) , 14600-14621 . |
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To address the key challenge encountered in fake news detection, i.e., multimodal data is difficult to be effectively semantically represented due to its intrinsic heterogeneity, this paper proposes a multimodal knowledge representation method for fake news detection. First, visual feature extraction is performed for fake news image data, the relevant images are sliced into multiple blocks, and then visual modal features are obtained by linear projection layer mapping. This simplifies the feature extraction process and reduces the computational cost, which helps to improve the fake news recognition performance. Second, to meet the actual fake news detection needs, a long text representation method based on topic words is investigated for the text data in fake news. Finally, the multimodal representation of the same fake news data is optimized by establishing a connection between two different modalities, visual and text, and inputting it into a BiLSTM-Attention based network to achieve the fusion of multimodal features. The experiment selects the same fake news data of EANN model and uses four classical classification methods to verify the effect of knowledge representation and compare it with the fusion model ViLT which is not optimized for long text. The experiment proves that the accuracy rate of fake news detection using the multimodal representation proposed in this paper is improved by 7.4% compared to the EANN model, and by 9.3% compared to the ViLT representation.
Keyword ：
fake news detection fake news detection feature extraction feature extraction feature fusion feature fusion multimodal representation multimodal representation
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| GB/T 7714 | Zeng, Fanhao , Yao, Jiaxin , Xu, Yijie et al. A Multimodal Knowledge Representation Method for Fake News Detection [J]. | 2024 4TH INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL AND ROBOTICS, ICCCR 2024 , 2024 : 360-364 . |
| MLA | Zeng, Fanhao et al. "A Multimodal Knowledge Representation Method for Fake News Detection" . | 2024 4TH INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL AND ROBOTICS, ICCCR 2024 (2024) : 360-364 . |
| APA | Zeng, Fanhao , Yao, Jiaxin , Xu, Yijie , Liu, Yanhua . A Multimodal Knowledge Representation Method for Fake News Detection . | 2024 4TH INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL AND ROBOTICS, ICCCR 2024 , 2024 , 360-364 . |
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SDN是一种被广泛应用的网络范式.面对DDoS攻击等网络安全威胁,在SDN中集成高效的DDoS攻击检测方法尤为重要.由于SDN集中控制的特性,集中式DDoS攻击检测方法在SDN环境中存在较高的安全风险,使得SDN的控制平面安全性受到了巨大挑战.此外,SDN环境中流量数据不断增加,导致复杂流量特征的更复杂化、不同实体之间严重的Non-IID分布等问题.这些问题对现有的基于联邦学习的检测模型准确性与鲁棒性的进一步提高造成严重阻碍.针对上述问题,本文提出了一种基于联邦增量学习的SDN环境下DDoS攻击检测模型.首先,为解决集中式DDoS攻击检测的安全风险与数据增量带来的Non-IID分布问题,本文提出了一种基于联邦增量学习的加权聚合算法,使用动态调整聚合权重的方式个性化适应不同子数据集增量情况,提高增量聚合效率.其次,针对SDN环境中复杂的流量特征,本文设计了一种基于LSTM的DDoS攻击检测方法,通过统计SDN环境中流量数据的时序特征,提取并学习数据的时序关特征的相关性,实现对流量特征数据的实时检测.最后,本文结合SDN集中管控特点,实现了SDN环境下的DDoS实时防御决策,根据DDoS攻击检测结果与网络实体信息,实现流规则实时下发,达到有效阻断DDoS攻击流量、保护拓扑重要实体并维护拓扑流量稳定的效果.本文将提出的模型在增量式DDoS攻击检测任务上与FedAvg、FA-FedAvg和FIL-IIoT三种方法进行性能对比实验.实验结果表明,本文提出方法相比于其他方法,在DDoS攻击检测准确率上提升5.06%～12.62%,F1-Score提升0.0565～0.1410.
Keyword ：
DDoS攻击检测 DDoS攻击检测 网络安全 网络安全 联邦增量学习 联邦增量学习 联邦学习 联邦学习 软件定义网络 软件定义网络
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| GB/T 7714 | 刘延华 , 方文昱 , 郭文忠 et al. 基于联邦增量学习的SDN环境下DDoS攻击检测模型 [J]. | 计算机学报 , 2024 , 47 (12) : 2852-2866 . |
| MLA | 刘延华 et al. "基于联邦增量学习的SDN环境下DDoS攻击检测模型" . | 计算机学报 47 . 12 (2024) : 2852-2866 . |
| APA | 刘延华 , 方文昱 , 郭文忠 , 赵宝康 , 黄维 . 基于联邦增量学习的SDN环境下DDoS攻击检测模型 . | 计算机学报 , 2024 , 47 (12) , 2852-2866 . |
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