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

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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A perturbation method to the tent map based on Lyapunov exponent and its application

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Chinese Physics B 2015年第10期24卷 78-85页

作者：曹绿晨罗玉玲丘森辉刘俊秀 guangxi key laboratory of multi-source information mining & security Faculty of Electronic EngineeringGuangxi Normal University School of Computing and Intelligent Systems University of Ulster

Perturbation imposed on a chaos system is an effective way to maintain its chaotic features. A novel parameter perturbation method for the tent map based on the Lyapunov exponent is proposed in this paper. The pseudo-random sequence generated by the tent map is sent to another chaos function - the Chebyshev map for the post processing. If the output value of the Chebyshev map falls into a certain range, it will be sent back to replace the parameter of the tent map. As a result, the parameter of the tent map keeps changing dynamically. The statistical analysis and experimental results prove that the disturbed tent map has a highly random distribution and achieves good cryptographic properties of a pseudo-random sequence. As a result, it weakens the phenomenon of strong correlation caused by the finite precision and effectively compensates for the digital chaos system dynamics degradation.

关键词： perturbation tent map Lyapunov exponent finite precision

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A Temporal Knowledge Graph Embedding Model Based on Variable Translation

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Tsinghua Science and Technology 2024年第5期29卷 1554-1565页

作者： Yadan Han Guangquan Lu Shichao Zhang Liang Zhang Cuifang Zou Guoqiu Wen key lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China and also with Guangxi Key Lab of Multi-Source Information Mining and SecurityGuangxi Normal UniversityGuilin 541004China

Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous *** is popularly used in knowledge graph completion(or link prediction)***-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)***,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet ***,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship ***,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge *** conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and *** model outperforms baseline models on multiple evaluation metrics according to the experimental results.

关键词： knowledge graph knowledge graph completion variable translation temporal properties link prediction triplet classification

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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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Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network

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Frontiers of Computer Science 2023年第6期17卷 87-99页

作者： Yufei ZENG Zhixin LI Zhenbin CHEN Huifang MA guangxi key lab of multi-source information mining and security Guangxi Normal UniversityGuilin 541004China College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspect words away from its related opinion words in a dependency ***,few models incorporate external affective knowledge for *** on this,we propose a novel architecture to tackle the above two limitations,while fills up the gap in applying heterogeneous graphs convolution network to ***,we employ affective knowledge as an sentiment node to augment the representation of ***,linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency ***,we design a multi-level semantic heterogeneous graph convolution network(Semantic-HGCN)to encode the heterogeneous graph for sentiment *** experiments are conducted on the datasets SemEval 2014 Task 4,SemEval 2015 task 12,SemEval 2016 task 5 and ACL 14 *** experimental results show that our method achieves the state-of-the-art performance.

关键词： heterogeneous graph convolution network multi-head attention network aspect-based sentiment analysis deep learning affective knowledge

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Efficient multi-scale community search method based on spectral graph wavelet

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Frontiers of Computer Science 2023年第5期17卷 89-101页

作者： Cairui YAN Huifang MA Qingqing LI Fanyi YANG Zhixin LI College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China guangxi key lab of multi-source information mining and security Guangxi Normal UniversityGuilin 541004China

Community search is an important problem in network analysis,which has attracted much attention in recent *** a query-oriented variant of community detection problem,community search starts with some given nodes,pays more attention to local network structures,and gets personalized resultant communities *** existing community search method typically returns a single target community containing query nodes by *** is a strict requirement and does not allow much *** many realworld applications,however,query nodes are expected to be located in multiple communities with different *** address this limitation of existing methods,an efficient spectral-based multi-Scale Community Search method(MSCS)is proposed,which can simultaneously identify the multi-scale target local communities to which query node *** MSCS,each node is equipped with a graph Fourier multiplier *** access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community *** addition,an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral *** experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

关键词： community search multi-scale spectral wavelets

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Community search over heterogeneous information networks via weighting strategy and query replacement

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Frontiers of Computer Science 2022年第4期16卷 231-233页

作者： Fanyi Yang Huifang Ma Weiwei Gao Zhixin Li College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China guangxi key lab of multi-source information mining and security Guangxi Normal UniversityGuilin541004China

1Introduction and main contributions Recently,community search over Heterogeneous information Networks(HINs)has attracted much attention in graph analysis,which aims to search for local communities containing query *** existing community search studies in HINs have proved effective in converting heterogeneous graphs to homogeneous graphs via pre-defined meta-paths with consistent head and tail node types,two major limitations ***,they fail to properly utilize the intermediate nodes to assign weights on the edges of the induced homogeneous graph.

关键词： utilize weights properly

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Synchronously scrambled diffuse image encryption method based on a new cosine chaotic map

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Chinese Physics B 2022年第8期31卷 296-309页

作者： Xiaopeng Yan Xingyuan Wang Yongjin Xian School of information Science and Technology Dalian Maritime UniversityDalian 116026China guangxi key laboratory of multi-source information mining&security Guangxi Normal UniversityGuilin 541004China

We present a new cosine chaotic mapping proved by chaos theory test and analysis such that the system has good cryptography properties, wide chaos range, simple structure, and good sensitivity to initial value, and the mapping can meet the needs of chaotic image encryption. Based on the cosine chaotic system, we propose a new encryption method. First,according to the cyclic characteristics of the mapping, the cyclic information wave is simulated. Second, the quasi-Doppler effect is used to synchronously scramble and diffuse the image to obfuscate the original pixel. Finally, the XOR diffusion of image pixels is carried out by information wave to further enhance the encryption effect. Simulation experiment and security analysis show that the algorithm has good security, can resist the common attack mode, and has good efficiency.

关键词： chaos mapping cosine mapping cyclic information wave doppler effect image encryption

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The Effects of Wettability on Primary Vortex and Secondary Flow in Three-Dimensional Rotating Fluid

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Communications in Theoretical Physics 2019年第12期71卷 1480-1484页

作者： Si-Hao Zhou Wen Qiu Yong Ye Bing He Bing-Hai Wen guangxi key lab of multi-source information mining&security Guangxi Normal UniversityGuilin 541004China Department of Computer Science and information Engineering Guangxi Normal UniversityGuilin 541004China

The secondary flow driven by the primary vortex in a cylinder,generating the so called"tea leaf paradox",is fundamental for understanding many natural phenomena,industrial applications and scientific *** this work,the effect of wettability on the primary vortex and secondary flow is investigated by the three-dimensional multiphase lattice Boltzmann method based on a chemical *** find that the surface wettability strongly affects the shape of the primary *** the increase of the contact angle of the cylinder,the sectional plane of the primary vortex gradually changes from a steep valley into a saddle with two raised *** the surface friction is reduced correspondingly,the core of the secondary vortex moves to the centerline of the cylinder and the vortex intensity also *** stirring force has stronger effects to enhance the secondary flow and push the vortex up than the surface ***,a small secondary vortex is discovered near the three-phase contact line when the surface has a moderate wettability,owing to the interaction between the secondary flow and the curved gas/liquid interface.

关键词： secondary flow lattice Boltzmann method multiphase flow rotating fluid

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DRNet:Towards fast,accurate and practical dish recognition

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Science China(Technological Sciences) 2021年第12期64卷 2651-2661页

作者： CHENG SiYuan CHU BinFei ZHONG BiNeng ZHANG ZiKai LIU Xin TANG ZhenJun LI XianXian guangxi key lab of multi-source information mining&security Guangxi Normal UniversityGuilin 541004China Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Seetatech Technology Nanjing 211800China

Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application *** this paper,we propose a practical two-stage dish recognition framework(DRNet)that yields a tradeoff between speed and accuracy while adapting to the variation in class *** the first stage,we build an arbitrary-oriented dish detector(AODD)to localize dish position,which can effectively alleviate the impact of background noise and pose *** the second stage,we propose a dish reidentifier(DReID)to recognize the registered dishes to handle uncertain *** further improve the accuracy of DRNet,we design an attribute recognition(AR)module to predict the attributes of *** attributes are used as auxiliary information to enhance the discriminative ability of ***,pruning and quantization are processed on our model to be deployed in embedded ***,to facilitate the study of dish recognition,a well-annotated dataset is *** AODD,DReID,AR,and DRNet run at about 14,25,16,and 5 fps on the hardware RKNN 3399 pro,respectively.

关键词： neural network acceleration neural network quantization object detection reidentification dish recognition

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