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学者姓名：刘延华
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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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面向烟草行业客户服务的数字化转型新需求，将互联网、GIS、多维关联分析等技术引入烟草公益活动的全生命周期管理中，研发了公益计划、茉莉地图、活动打卡、客户管理等关键模块。结果表明，该公益活动平台实现了公益活动与客户服务的数字化整合，能够实时掌握烟草公益活动态势信息，探索了“公益+客户服务”的新模式，有力赋能烟草行业数字化转型。
Keyword ：
GIS GIS 公益活动 公益活动 关联分析 关联分析 客户服务 客户服务 烟草行业 烟草行业
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| GB/T 7714 | 林立兰 , 许少康 , 刘延华 . 基于GIS的烟草公益活动智慧服务平台设计 [J]. | 电脑与信息技术 , 2024 , 32 (02) : 46-50 . |
| MLA | 林立兰 et al. "基于GIS的烟草公益活动智慧服务平台设计" . | 电脑与信息技术 32 . 02 (2024) : 46-50 . |
| APA | 林立兰 , 许少康 , 刘延华 . 基于GIS的烟草公益活动智慧服务平台设计 . | 电脑与信息技术 , 2024 , 32 (02) , 46-50 . |
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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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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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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%. © 2024 Tech Science Press. All rights reserved.
Keyword ：
DDoS DDoS DNN DNN improved generalized entropy improved generalized entropy real-time real-time
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| GB/T 7714 | Liu, Y. , Han, Y. , Chen, H. et al. IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN [J]. | Computers, Materials and Continua , 2024 , 80 (2) : 1851-1866 . |
| MLA | Liu, Y. et al. "IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN" . | Computers, Materials and Continua 80 . 2 (2024) : 1851-1866 . |
| APA | Liu, Y. , Han, Y. , Chen, H. , Zhao, B. , Wang, X. , Liu, X. . IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN . | Computers, Materials and Continua , 2024 , 80 (2) , 1851-1866 . |
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Internet-of-Things (IoT) devices are increasingly used in people’s lives and production in various industries. To detect and defend against Denial-of-Service (DDoS) attacks that occur on IoT networks, a lot of methods based on machine learning and deep learning have been proposed in recent years. However, these methods usually do not consider the limitation of computational resources of IoT devices. In this paper, we propose an edge model DDoS-Attack-Guard (DAG) based on Bi-GRU and ShuffleNet for DDoS identification and classification with the target of lightweight and real-time. To demonstrate the performance of our models, we use the CICDDoS2019 dataset to test the identification and classification accuracy as well as the model inference time. In addition, we build a multi-layer coder-decoder structure that can extract the potential temporal contextual features of DDoS traffic, and introduce a reconstruction structure that can improve model training. Through ablation experiments and comparative experiments, our model has an average inference speed of 2.5 ms across different data sizes, which is 50% faster than the Sota method, while hitting 99.3% and 99.9% accuracy in identification and classification respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
DDoS Attacks DDoS Attacks Deep Learning Deep Learning Edge Defense Edge Defense IoT security IoT security Reconstruction Structure Reconstruction Structure
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| GB/T 7714 | Liu, Y. , Chen, C. , Zhang, Q. et al. DAG: A Lightweight and Real-Time Edge Defense Model for IoT DDoS Attacks [未知]. |
| MLA | Liu, Y. et al. "DAG: A Lightweight and Real-Time Edge Defense Model for IoT DDoS Attacks" [未知]. |
| APA | Liu, Y. , Chen, C. , Zhang, Q. , Zeng, F. , Liu, X. . DAG: A Lightweight and Real-Time Edge Defense Model for IoT DDoS Attacks [未知]. |
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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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提出一种基于多维关联度的数字证据链自动构造方法.首先设计一种数字证据标准化表示方法，将数字事件与其之间的关联关系进行规范化描述.然后通过对数字事件关联关系的分析，提出多维度的非因果关联度计算方法和基本证据环的构造方法，对数字事件因果关系进行深度分析.实验结果表明，所提出方法构造的数据证据链对于提升数字证据的证明力具有一定的应用意义.
Keyword ：
关联度 关联度 数字取证 数字取证 数字证据链 数字证据链 证据环 证据环
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| GB/T 7714 | 刘延华 , 欧振贵 , 刘西蒙 et al. 多维关联度分析驱动的数字证据链构造方法 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (04) : 404-412 . |
| MLA | 刘延华 et al. "多维关联度分析驱动的数字证据链构造方法" . | 福州大学学报(自然科学版) 52 . 04 (2024) : 404-412 . |
| APA | 刘延华 , 欧振贵 , 刘西蒙 , 陈惠文 , 林钟馨 , 张明辉 . 多维关联度分析驱动的数字证据链构造方法 . | 福州大学学报(自然科学版) , 2024 , 52 (04) , 404-412 . |
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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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Network intrusion detection system plays a crucial role in protecting the integrity and availability of sensitive assets, where the detected traffic data contain a large amount of time, space, and statistical information. However, existing research lacks the utilization of spatial-temporal multi-granularity data features and the mutual support among different data features, thus making it difficult to specifically and accurately identify anomalies. Considering the distinctions among different granularities, we propose a framework called tri-broad learning system (TBLS), which can learn and integrate the three granular features. To explore the spatial-temporal connotation of the traffic information accurately, a feature dataset containing three granularities is constructed according to the characteristics of time, space, and data content. In this way, we use broad learning basic units to extract abstract features of different granularities and then express these features in different feature spaces to enhance them separately. We use a normal distribution initialization method in BLS to optimize the weights of feature nodes and enhancement nodes for better detection accuracy. The merits of our proposed model are exhibited on the UNSW-NB15, CIC-IDS-2017, CIC-DDoS-2019, and mixed traffic datasets. Experimental results show that TBLS outperforms the typical BLS in terms of various evaluation metrics and time consumption. Compared with other machine learning methods, TBLS achieves better performance metrics.
Keyword ：
Broad learning system Broad learning system Network intrusion detection Network intrusion detection Spatial-temporal multi-granularity Spatial-temporal multi-granularity Traffic information Traffic information
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| GB/T 7714 | Li, Jieling , Zhang, Hao , Liu, Zhihuang et al. Network intrusion detection via tri-broad learning system based on spatial-temporal granularity [J]. | JOURNAL OF SUPERCOMPUTING , 2023 , 79 (8) : 9180-9205 . |
| MLA | Li, Jieling et al. "Network intrusion detection via tri-broad learning system based on spatial-temporal granularity" . | JOURNAL OF SUPERCOMPUTING 79 . 8 (2023) : 9180-9205 . |
| APA | Li, Jieling , Zhang, Hao , Liu, Zhihuang , Liu, Yanhua . Network intrusion detection via tri-broad learning system based on spatial-temporal granularity . | JOURNAL OF SUPERCOMPUTING , 2023 , 79 (8) , 9180-9205 . |
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