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review on Security defense Technology research in Edge Computing Environment

Chinese Journal of Electronics 2024年第1期33卷 1-18页

作者： Ke SHANG Weizhen HE Shuai ZHANG National Digital Switching System Engineering and Technological R&D Center

Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the Internet of things. The new network architecture of edge computing brings new security challenges. Based on this, this paper investigates the edge computing security literature published in recent years and summarizes and analyzes research work on edge computing security from different attack surfaces. We start with the definition and architecture of edge computing. From the attack surface between device and edge server, as well as on edge servers, the research describes the security threats and defense methods of edge computing. In addition, the cause of the attack and the pros and cons of defense methods is introduced. The challenges and future research directions of edge computing are given.

关键词： Edge computing Security Threat defense Architecture Attack surface

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Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

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Chinese Journal of Electronics 2024年第6期33卷 1412-1420页

作者： Binhao HU Jianpeng ZHANG Hongchang CHEN School of Cyber Science and Engineering Zhengzhou University National Digital Switching System Engineering & Technological R&D Center (NDSC)

With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality of the knowledge graphs. To improve the quality of knowledge graphs, translation-based graph embeddings such as TransE, learn structural information by representing triples as low-dimensional dense vectors. However, it is difficult to generalize to the unseen entities that are not observed during training but appear during testing. Other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples. Although they are robust to incompleteness, they need to calculate the score of all candidate entities for each triple during inference. We consider combining two models to enhance the robustness of unseen entities by semantic information, and prevent combined explosion by reducing inference overhead through structured information. We use a pre-training language model to code triples and learn the semantic information within them, and use a hyperbolic space-based distance model to learn structural information, then integrate the two types of information together. We evaluate our model by performing link prediction experiments on standard datasets. The experimental results show that our model achieves better performances than state-of-the-art methods on two standard datasets.

关键词： Training Semantics Knowledge graphs Predictive models Vectors robustness Explosions Standards Testing Context modeling

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MMCSd:Multi-Modal Knowledge Graph Completion Based on Super-resolution and detailed description Generation

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Computers, Materials & Continua 2025年第4期83卷 761-783页

作者： Huansha Wang ruiyang Huang Qinrang Liu Shaomei Li Jianpeng Zhang National Digital Switching System Engineering&Technological R&D Center Information Engineering UniversityZhengzhou450001China

Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown *** to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual ***,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality *** this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased *** address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and detailed description Generation(MMCSd).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual ***,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual ***,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual *** conducted experiments on FB15K-237 and dB13K,and the results showed that MMCSd can effectively perform MMKGC and achieve state-of-the-art performance.

关键词： Multi-modal knowledge graph knowledge graph completion multi-modal fusion

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radar Signal deinterleaving Based on Hidden Markov Chains and residual Fence Networks

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IEEE Transactions on Aerospace and Electronic systems 2024年第3期61卷 6011-6025页

作者： Xie, Min Huang, Jie Zhao, Chuang Hu, de-Xiu Sun, Yi-Fan National Digital Switching System Engineering & Technological R&D Center Zhengzhou450000 China

For prior-knowledge-informed scenarios, this paper proposes a radar signal deinterleaving method based on hidden Markov chains and residual fence networks (rFNs) with enhanced applicability for different pulse repetition interval (PrI) types and improved performance under PrI jitter. The proposed approach accommodates the five main PrI modulation types by modeling interleaved pulse streams as hidden Markov models (HMMs). Pulse deinterleaving is transformed into a state-sequence prediction problem using HMMs and further into a path optimization problem within rFNs. This method utilizes global information for reliable sequence separation. Experimental results indicate that the method effectively deinterleaves fixed, staggered, dwell-And-switch, sliding, and wobulated sequences under unintentional jitter, significantly improving performance under intentional jitter. The practical value and applicability of the method are demonstrated, providing a new perspective for deinterleaving. © 1965-2011 IEEE.

关键词： Hidden Markov models

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LdAS&ET-Ad:Learnable distillation Attack Strategies and Evolvable Teachers Adversarial distillation

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Computers, Materials & Continua 2024年第5期79卷 2331-2359页

作者： Shuyi Li Hongchao Hu Xiaohan Yang Guozhen Cheng Wenyan Liu Wei Guo National Digital Switching System Engineering&Technological R&D Center The PLA Information Engineering UniversityZhengzhou450000China

Adversarial distillation(Ad)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial ***,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for ***,the reliability of guidance from static teachers diminishes as target models become more *** paper proposes an Ad method called Learnable distillation Attack Strategies and Evolvable Teachers Adversarial distillation(LdAS&ET-Ad).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the Ad ***,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target *** calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers *** evaluate the performance of LdAS&ET-Ad against different adversarial attacks on the CIFAr-10 and CIFAr-100 *** experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAr-10 dataset for resNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline *** comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial distillation,the top-performing method in terms of robustne

关键词： Adversarial training adversarial distillation learnable distillation attack strategies teacher evolution strategy

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development paradigms of cyberspace endogenous safety and security

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Science China(Information Sciences) 2022年第5期65卷 264-266页

作者： Jiangxing WU National Digital Switching System Engineering & Technological R&D Center

Scientifc interpretation of paradigm. The concept and theory of paradigm were first proposed by Thomas Kuhn, a famous American science philosopher, and were systematically described in his book “The Structure of Scie... 详细信息

关键词： cyberspace endogenous security ubiquitous problem unknown unknown security threats relatively-correct axiom dynamic heterogeneous redundancy architecture cyberspace endogenous safety and security development paradigm

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Cyberspace Endogenous Safety and Security

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engineering 2022年第8期8卷 179-185页

作者： Jiangxing Wu National Digital Switching System Engineering&Technological R&D Center Zhengzhou 450002China

Uncertain security threats caused by vulnerabilities and backdoors are the most serious and difficult problem in *** paper analyzes the philosophical and technical causes of the existence of so-called"dark functions"such as system vulnerabilities and backdoors,and points out that endogenous security problems cannot be completely eliminated at the theoretical and engineering levels;rather,it is necessary to develop or utilize the endogenous security functions of the system architecture *** addition,this paper gives a definition for and lists the main technical characteristics of endogenous safety and security in cyberspace,introduces endogenous safety and security mechanisms and characteristics based on dynamic heterogeneous redundancy(dHr)architecture,and describes the theoretical implications of a coding channel based on dHr.

关键词： Cyberspace endogenous security problem Uncertain threat Cyberspace endogenous safety and security relative right axiom dynamic heterogeneous redundant architecture

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On Time-delay Power divider 8

On Time-Delay Power Divider

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8th International Conference on Electronic Technology and Information Science, ICETIS 2023

作者： Liu, Zheng Zhang, Jian Lei, Xue National Digital Switching System Engineering and Technological R&D Center China

ISBN: (纸本)9781510666535

In this paper a 1 to 4 time-delay power-divider was provided. The phase and amplitude relation between each port was simulated. The time-delay between the port was 0.016 ns in the x-direction and 0.16 ns in the x-direction. The maximum amplitude difference between each port was 1 dB and the maximum phase difference error was ±6°. The S11 value was lower than -20 dB. The proposed time delay power divider can be applied to VICTS antenna to enhance the instantaneous bandwidth. © 2023 SPIE.

关键词： Time delay

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Prioritized recovery Strategy For robust UAV Swarm Communication via Graph reinforcement Learning

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IEEE Internet of Things Journal 2025年第13期12卷 23891-23904页

作者： Peng, Yabin Wu, Jiangxing duan, Tong Liu, Yuchen Zhang, Zhen Zhang, Jinfeng Southeast University School of Cyber Science and Engineering Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China National Digital Switching System Engineering and Technological R and D Center Zhengzhou450002 China PLA Information Engineering University Zhengzhou450002 China

Network failures, whether due to random disruptions or malicious attacks, pose significant challenges for unmanned aerial vehicle (UAV) swarm networks. One critical concern is determining which failed UAVs to recover or replace under limited resource conditions to enhance the robustness of their communication networks. Current research primarily considers static structural characteristics of the network and struggles to uncover deep features that influence network robustness, and the efficiency cannot meet the real-time needs in UAV swarm scenarios. To address these issues, we introduce a Prioritized recovery strategy for failed nodes based on Graph reinforcement Learning (PrGrL). This approach integrates a random SAmpling neighbor method with a multi-head attention mechanism to create a novel Graph Convolutional Kernel (SAGCK). This kernel is designed to extract global structural information and relative positional information of nodes within the graph. Additionally, we develop a deep Policy Network (dPN) that explores the intricate relationships between graph-level and node embedding features, enabling the assessment of nodes’ impact on overall robustness. PrGrL’s network parameters are automatically updated and optimized using scalable deep reinforcement learning. Importantly, PrGrL prioritizes the recovery of boundary nodes within connected components to enhance network robustness further. Our experiments, conducted on both simulated and real-world networks, demonstrate that PrGrL outperforms existing methods of robustness enhancement across various recovery ratios, attack strategies, and network sizes while delivering superior real-time performance. © 2014 IEEE.

关键词： deep reinforcement learning

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A Malicious UrL detection Method Based on Bert-CNN 5

A Malicious URL Detection Method Based on Bert-CNN

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5th International Conference on Electronic engineering and Informatics, ICEEI 2023

作者： Jin, Lixiao Huang, ruiyang Zhang, Xuanming Wan, Fangjie School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&d Center Zhengzhou China

ISBN: (纸本)9781643685021

UrL detection is an important task in the field of Internet security. To address the problems of low model accuracy and weak generalization ability to exist machine-learning-based malicious UrL detection methods, a UrL classification method based on a combination of the BErT model and the convolutional neural network (CNN) is proposed. First, a regular expression-based approach is used to achieve pre-processing of UrL data;second, a malicious UrL classification method combining BErT pre-training model and CNN is designed to effectively handle contextual relevance and local features in malicious UrL detection tasks;finally, experiments are conducted on a dataset of more than 30,000 UrLs to evaluate its performance and efficiency. The experimental results show that the Bert-CNN model outperforms other deep-learning-based and traditional methods in terms of accuracy, and our model achieves a 99.5% accuracy on the test dataset. © 2024 The Authors.

关键词： Classification (of information)

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