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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 laboratory 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 information 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 information 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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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 laboratory 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 CICDDoS2019 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 2019 *** 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 theF1scorehas 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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Electrocatalytic cleavage of a lignin β-O-4 model compound and coupling with nitrogen-containing aromatics using Prussian blue analogue-derived nickel–cobalt spinel

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Journal of Energy Chemistry 2025年第1期100卷 628-636页

作者： Yi Qi Xueying Chen Bowen Liu Xuliang Lin Xueqing Qiu Yanlin Qin Guangdong Provincial Key laboratory of Plant Resources Biorefinery School of Chemical Engineering and Light Industry Guangdong University of Technology Guangdong Provincial laboratory of Chemistry and Fine Chemical engineering Jieyang Center Guangdong Basic Research Center of Excellence for Ecological security and Green Development Guangdong University of Technology

Electrochemical conversion of lignin for the production of high-value heterocyclic aromatic compounds has great *** demonstrate the targeted synthesis and cation modulation of NiCo2O4spinel nanoboxes,synthesized via cation exchange and calcination *** catalysts exhibit excellent efficacy in the electrocatalytic conversion of lignin model compounds,specifically 2-phenoxy-1-phenylethanol,into nitrogen-containing aromatics,achieving high conversion rates and *** catalysts were synthesized via a cation exchange and calcination oxidation process,using Prussian blue nanocubes as *** porous architecture and polymetallic composition of the NiCo2O4spinel demonstrated superior performance in electrocatalytic oxidative coupling,achieving a 99.2 wt%conversion rate of the 2-phenoxy-1-phenylethanol with selectivities of 37.5 wt%for quinoline derivatives and 31.5 wt%for *** innovations include the development of a sustainable one-pot synthesis method for quinoline derivatives,the elucidation of a multistage reaction pathway involving CAO bond cleavage,hydroxyaldol condensation,and CAN bond formation,and a deeper mechanistic understanding derived from DFT *** work establishes a new strategy for lignin valorization,offering a sustainable route to produce high-value nitrogen-containing aromatics from renewable biomass under mild conditions,without the need for additional reagents.

关键词： Spinel Biomass valorization Electrocatalysis Lignin oxidation Coupling

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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 laboratory 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 collect475192 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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CO2-mediated bicarbonate conversion to concentrated formate in a CEM-based electrolyzer

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Journal of Energy Chemistry 2025年第1期100卷 605-611页

作者： Haocheng Xiong Donghuan Wu Haonan Li Andrew Li Qikun Hu Siyao Song Bingjun Xu Qi Lu State Key laboratory of Chemical engineering Department of Chemical Engineering Tsinghua University College of Chemistry and Molecular engineering Peking University Ordos laboratory

Renewable energy-driven bicarbonate conversion to valuable chemicals presents an attractive strategy for mitigating CO2emissions,as bicarbonate can be efficiently generated from the capture of atmospheric CO2using alkaline solutions with reactive *** this work,we present a CO2-mediated bicarbonate conversion to pure formate using a cation exchange membrane-based electrolyzer with a 25 cm2electrode *** electrolysis achieved selectivities exceeding 75%for formate at a total current of 2.5 A,achieving formate concentrations up to 1.2 M and yields as high as 95%over extended *** techno-economic assessment confirmed the economic viability of the process,highlighting the potential for bicarbonate electrolysis as a sustainable method for producing valuable chemicals.

关键词： Electrocatalysis Techno-economic assessments Electrochemistry Flow electrolyzer

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An efficient quantum proactive incremental learning algorithm

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Cyberspace security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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A self-adaptive inorganic in-situ separator by particle crosslinking for nonflammable lithium-ion batteries

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Journal of Energy Chemistry 2025年第1期100卷 469-480页

作者： Jiarui Yang Jiuzhou Liu Wenrui Cai Ziyu Zhao Shan Wang Lu He Shanshan Lv Zhiwei Zhu Zhongfeng Ji Guojiang Wen Hua Li Yuanming Zhai Xuewei Fu Wei Yang Yu Wang College of Polymer Science and engineering State Key Laboratory of Polymer Materials Engineering Sichuan University

All-safe liquid-state lithium-ion batteries(ASLS-LIBs) is of great interest as they can potentially combine the safety of all-solid-state batteries with the high performance and low manufacturing cost of traditional liquid-state LIBs. However, the practical success of ASLS-LIBs is bottlenecked by the lack of advanced separator technology that can simultaneously realize high performances in puncturing-tolerability,fire-resistance, and importantly, wetting-capability with non-flammable liquid-electrolytes. Here, we propose a concept of inorganic in-situ separator(IISS) by hybrid-sol physical crosslinking directly onto the electrode surface to address the above challenges. Particularly, the hybrid-sol is designed with silica nanoparticles as the building block and poly(vinylidene difluoride) nanoparticles as the crosslinking agent. The critical factors for controlling the IISS microstructures and properties have been systematically investigated. The advantages of the IISS have been confirmed by its fast wetting with various fireresistant liquid-electrolytes, customizable thickness and porous structures, robust interface with planar or three-dimensional(3D)-structured electrodes, and importantly, unexpected self-adaptability against puncturing. Enabled by the above merits, a fire-resistant ASLS-LIB is successfully assembled and demonstrated with stable electrochemical performance. This sol-crosslinked IISS may open an avenue for the studies on the next-generation separator technology, cell assembling, solid electrolyte processing as well as non-flammable secondary batteries.

关键词： Silica nanoparticles

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Precise gradient separation of nanoparticles using reduced graphene oxide-polyoxomolybdate membranes

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Science China Chemistry 2025年第1期68卷 209-216页

作者： Yixin Yang Wanlei Zhao Ruoxuan Zheng Hongqiang Li Wei Chen Yu-Fei Song State Key laboratory of Chemical Resource engineering Beijing University of Chemical Technology Quzhou Institute for Innovation in Resource Chemical engineering

The purification and size sieving of nanoparticles （NPs） represents a significant scientific ***,the r GO-polyoxometalates (POMs=PMo12,Mo132and Mo368) membranes were fabricated successfully via a simple and scalable POMsassisted in-situ photoreduction *** the mass ratio of r GO to PMo12was 1:30,the resultant r GO-PMo12membranes with highly ordered and uniform nanochannels of 1.0 nm can be obtained,which showed an accurate carbon quantum dots（CQDs） separation with a cut-off size approximately 1.0 *** increase of the size of the interlayered POMs from PMo12（1.0 nm）,{Mo132}（2.9 nm） to{Mo368}（3.8 nm）,the resultant r GO-PMo12,r GO-Mo132and r GO-Mo368exhibited precise gradient separation of CQDs and Au nanoparticles by size exclusion *** results pave a promising way to construct new twodimensional （2D） membranes for highly efficient size-selective separation.

关键词： reduced graphene oxide polyoxometalates membranes gradient separation nanoparticles

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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 Key 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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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical engineering and Automation Luoyang Institute of Science and Technology School of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

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