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Malicious Document Detection Based on GGE Visualization

Computers, Materials & Continua 2025年第1期82卷 1233-1254页

作者： Youhe Wang Yi Sun Yujie Li Chuanqi Zhou Henan Province key {1. of information security Information Engineering UniversityZhengzhou450000China

With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly ***,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and *** address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the {1. entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial {1. stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color *** Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE *** shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.

关键词： Malicious document visualization EfficientNet-B0 convolutional block attention module GGE image

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Quantum public-key crypto via EPR pairs

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 58-63页

作者： Xiang-Qun Fu Hong-Wei Li Jian-Hong Shi Tan Li Wan-Su Bao Henan key {1. of Quantum information and Cryptography Information Engineering University Synergetic Innovation Center of Quantum information and Quantum Physics University of Science and Technology of China

With the development of quantum computing technology, quantum public-{1. cryptography is gradually becoming an alternative to the existing classical public-{1. cryptography. This paper designs a quantum trapdoor one-way function via EPR pairs and quantum measurements. Based on this, a new quantum public-{1. cryptosystem is presented, which offers forward security,and can resist the chosen-plaintext attack and chosen-ciphertext attack. Compared with the existing quantum public-{1. cryptos,eavesdropping can be automatically detected in this new quantum public-{1. cryptosystem under a necessary condition, which is also detailed in the paper.

关键词： quantum public-key crypto trapdoor one-way function EPR pairs

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A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System security {1. laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and security Center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

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Computers, Materials & Continua 2025年第1期82卷 645-674页

作者： Chunhui Li Xiaoying Wang Qingjie Zhang Jiaye Liang Aijing Zhang School of information Engineering Institute of Disaster PreventionLangfang065201China Langfang key {1. of Network Emergency Protection and Network security Langfang065201China

Previous studies have shown that deep learning is very effective in detecting known ***,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low ***,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS201. dataset,and its performance is evaluated on a new GINKS2023 *** hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971.in the CICDDoS 201. *** evaluation accuracy of the new dataset GINKS2023 created in this paper is *** to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81.,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1.corehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1.score.

关键词： Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm

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Prospects on mangrove knowledge services in the smart era: From plant atlas to knowledge graphs

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Science China Earth Sciences 2025年第1期68卷 111-127页

作者： Zhi-Wei HOU Wenlong JING Cheng-Zhi QIN Ji YANG Qing XIA Xiaoling YIN key {1. of Guangdong for Utilization of Remote Sensing and Geographical information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Southern Marine Science and Engineering Guangdong laboratory (Guangzhou) State key {1. of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences School of Traffic & Transportation Engineering Changsha University of Science and Technology

Mangroves are crucial to the ecological security of the Earth and human well-being. Their management, conservation, and restoration are of great importance and necessitate the support of spatio-temporal {1. and multidisciplinary knowledge such as biology and ecology. Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves. However, this kind of service is oriented to {1. retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data, facing the problem of “massive data, rare knowledge”. Knowledge graphs are capable of extracting, organizing, and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers. They provide a solution for the realization of intelligent knowledge services. Focusing on the urgent need for mangrove knowledge acquisition, formal representation, and intelligent services, this paper proposes a research prospect on mangrove knowledge graphs and knowledge services. We first analyze the similarities and differences between various domain-specific concepts of Tupu. On this basis, we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal {1. with mangrove ecosystems as the core. Then, we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services, including multiple research levels such as ubiquitous data sensing and aggregation, knowledge organization and graph construction, and intelligent mangrove knowledge services. Subsequently, the methods and workflow for constructing mangrove knowledge graphs are introduced. Finally, we discuss the challenges and possible future directions of mangrove knowledge services in the smart era, including the construction of a mangrove knowledge system that integra

关键词： Mangrove Tupu Knowledge graph Multi-modal Knowledge service

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E-PRedictor: an approach for early prediction of pull request acceptance

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Science China(information Sciences) 2025年

作者： Kexing CHEN Lingfeng BAO Xing HU Xin XIA Xiaohu YANG State key {1. of Blockchain and Data security Zhejiang University Software Engineering Application technology Lab

A pull request (PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software (OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while (e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor (earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features (i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect4751.2 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models (e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1.Merge, F1.Reject, and AUC (area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1., 60.5%, and 85.4%, respectively.

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Analysis and solution of streak effect in high dynamic range CMOS image sensors

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Science China(information Sciences) 2025年第1期68卷 402-403页

作者： Wanbin ZHA Jiangtao XU Jinghua AO Kaiming NIE Zhiyuan GAO School of Microelectronics Tianjin University Tianjin key {1. of Imaging and Sensing Microelectronic technology Tianjin University

The high dynamic range (HDR) CMOS image sensor has been widely used in many fields,such as autonomous driving and security guarding [1..The dual conversion gain (DCG)structure,which adjusts the conversion gain (CG) by changing the capacitors connected to the floating diffusion (FD)nodes in the pixel,is an effective and prevalent method for realizing HDR imaging [2].While the DCG structure can extend the dynamic range,in the practical HDR imaging process,the substantial number of photo-generated electrons in bright areas can cause significant voltage variations in the pixel *** variations affect the gray values in darker areas of the same image,leading to the streak effect [3].This study primarily investigates the streak effect caused by forward conduction current of the PN junction in DCG pixels,and proposes a circuit to suppress this effect.

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Automatic search method for multiple differentials and its application on MANTIS

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Science China(information Sciences) 2019年第3期62卷 145-159页

作者： Shiyao CHEN Ru LIU Tingting CUI Meiqin WANG key {1. of cryptologic technology and information security Ministry of EducationShandong University

Multiple differential cryptanalysis is one of the extensions of classic differential cryptanalysis. In this paper, we present a generic automatic search method for clustering multiple differentials on a target block cipher. Our search method has two steps. Firstly, the sets of input and output differences will be *** these sets, we get different multiple differentials. Then for each one of these multiple differentials, we enumerate and record all satisfied differential trails, which leads to a more accurate evaluation of the multiple differentials distinguisher. Among these different multiple differentials distinguishers, we can choose the best one for key recovery attack. We demonstrate our search method by applying it on the part of differentials of the block cipher MANTIS. As a result, we find a new 1.-round multiple differentials distinguisher with probability 2;and an 1.-round multiple differentials distinguisher with probability 2;, which is the longest distinguisher for MANTIS so far as we know. This new 1.-round distinguisher can lead to a better signal-to-noise ratio, so we derive an improved key recovery attack on MANTIS-6 with the complexity of about 2;chosen-plaintext queries, 2;encryptions and data-time product 2;, which is better than the previous best one with data-time product 2;. Aiming at exploring the gap between the performance of multiple differential attack and the security margin on MANTIS, we also use the 1.-round distinguisher to derive a key recovery attack on MANTIS-7 with the complexity of about 2;chosen-plaintext queries,2;encryptions and data-time product 2;. It does not threat the security of full version MANTIS（MANTIS-7） since the security bound of data-time product claimed by the designers is 2;.

关键词： multiple di?erential cryptanalysis structure attack automatic search MANTIS MILP

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and information {1. Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State key {1. of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data {1. and privacy *** research emphasizes data {1. and user privacy concerns within smart ***,existing methods struggle with efficiency and {1. when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and {1. by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,{1.,and privacy protection capabilities are validated through rigorous {1. proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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