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Social User Geolocation Method Based on POI Location Feature Enhancement in Context 1

30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Liu, Yu Qiao, Yaqiong Liu, Yimin Du, Shaoyong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (数字)9783031687754

ISBN: (纸本)9783031687747

The geolocation information in social media user generated text is one of the important bases for inferring users' location. The existing methods often rely on the mentioned Point of Interest (POI) and location Indicative word in user generated text as the inference basis, in consideration of the lack of consideration of semantic information and the inaccuracy or incompleteness of POI contribute to the inaccuracy of user geolocation based on text. To address these issues, this paper proposes Social User Geolocation Method based on POI Location Feature Enhancement in Context (UGLE). Firstly, the POI in user-generated text is removed by deep neural network. Then, through three geographical traits of POI: location orientation, word frequency and word length, the problem of ambiguous POI location orientation in user generated text is solved. Finally, by combining the semantic information of the words around POI, the user vector with enhanced location features is generated, which improves the geolocation accuracy of social users. On the Weibo dataset, the proposed Method is compared with four existing typical methods, PaQL, SeFG, MNB-PART, and MNB-LGR. The results show that the proposed method's accuracy in inferring province-level and city-level locations is 76.78% and 62.56%, respectively. These results are 6.81% and 6.66% higher than the existing best results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Path test data generation using adaptive simulated annealing particle swarm optimization

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Soft computing 2024年第17-18期28卷 9587-9607页

作者： Jiao, Chongyang Zhou, Qinglei State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China Henan Information Engineering School Zhengzhou Vocational College of Industrial Safety Zhengzhou450000 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

Software testing is an effective means of ensuring software quality. The cost of software testing is the main component of the total cost of software development. The generation of test data is very important in testing, because the efficiency of testing depends on the test data used. A significant part of software testing automation is the test data generation. How to automatically generate test datasets is still an open problem. Search-based software testing (SBST) is a process of generating test data that employs meta-heuristic methods to resolve tough NP-hard problems. The test data generation problem belongs to NP-hard problems. This paper focuses on the automatic generation of test data for path coverage based on control flow criteria in structural testing. Some meta-heuristic algorithms have been used to search for software test data. To advance the efficiency and effectiveness of path test data generation, an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed. The probabilistic jumping property of simulated annealing (SA) algorithm is introduced into the particle swarm optimization (PSO) algorithm to make the particle accept bad solution at a certain probability during the searching process, and therefore boost the capacity of the proposed algorithm to bounce from the local optimum. The fitness function is constructed by the branch function superposition to properly guide the search process. In addition, to improve the convergence speed of the algorithm, an adaptive adjustment scheme based on inertial weight and an adjustment scheme based on learning factor are proposed as well. The experimental results show that the proposed method can effectually avoid premature convergence and effectively upgrades the efficiency of generating test data automatically. It is also competitive in solving other complicated optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Natur

关键词： Software testing

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A Vulnerability Automation Exploitation Method Based on Symbolic Execution

A Vulnerability Automation Exploitation Method Based on Symb...

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2023 International Conference on Electronic Information engineering and Data Processing, EIEDP 2023

作者： Ge, Xueshuai Liu, Tieming Xie, Yaobin Zhang, Yuanyuan Cyberspace Security Department State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information service room Unit 32738 China

ISBN: (纸本)9781510666160

With increasing number of software vulnerabilities, the quantity of attacks utilizing malicious samples is also on rise, leading to intensified adversarial competition. In particular, the application of automatic vulnerability mining techniques has resulted in a significant increase in the number of vulnerabilities, but security researchers often do not have enough time to deal with them. This paper proposes a symbol-execution-based automated vulnerability exploitation method, which can achieve automation of vulnerability detection, classification and exploitation. Finally, this paper designs and implements a prototype system for symbol-execution-based automated vulnerability exploitation, and verifies its effectiveness through experiments. This research provides security analysts with an in-depth understanding of the types of vulnerabilities and determines methods for vulnerability exploitability, further improving the efficiency of analyzing and fixing vulnerabilities. © 2023 SPIE.

关键词： Automation

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A Survey of Automatic Exploitation of Binary Vulnerabilities

A Survey of Automatic Exploitation of Binary Vulnerabilities

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2023 International Conference on Computer Network Security and Software engineering, CNSSE 2023

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

The mining and exploitation of security vulnerabilities have always been the focus of offensive and defensive confrontations. In recent years, with the application of technologies such as fuzzing in vulnerability mining, the number of discovered vulnerabilities continues to increase, which seriously threatens the security of the network world. With so many vulnerabilities, it is unrealistic to rely solely on manual analysis by security experts, which is not only costly, but also time-consuming, and cannot meet the needs of vulnerability assessment and defense. Therefore, an automated exploit solution for vulnerabilities is urgently needed to solve the problem of low efficiency of manual vulnerability assessment. Existing research has proposed some solutions and achieved certain results. The purpose of this paper is to review and analyze the existing typical research results. © 2023 SPIE.

关键词： Network security

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Automated Quantum Volume Test 2

Automated Quantum Volume Test

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2022 2nd International Conference on Electronics, Circuits and Information engineering, ECIE 2022

作者： Liu, Xiaonan He, Ming Wang, Junchao Xie, Haoshan Zhao, Chenyan Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

As a benchmark for the overall performance of quantum computers, quantum volume has the advantage of being able to reflect the depth of running quantum circuits. But, the quantum volume test code provided by IBM needs to be executed manually, and the simulation result of the quantum simulator is used as the result of the volume test, so that users cannot quickly and accurately test the quantum volume of the actual quantum computer required. In response to this problem, this paper designs an automated quantum volume test program. The program automatically generates quantum volume sequences, selects the number of executions of quantum circuits, and defines real quantum computers to facilitate users to perform quantum volume tests on quantum computers provided by the IBM Quantum Cloud Platform. Simultaneously, according to the automated test program, the quantum volume of IBM's four small superconducting quantum computers was tested. The test results show that (1) the quantum computer is different, and the qubit layout and execution times ntrials are the same, will cause the quantum volume is uncertain;(2) the same quantum computer, whether ntrials is the same, the robustness of qubit coupling will be affected to a certain extent. © Published under licence by IOP Publishing Ltd.

关键词： Benchmarking

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Research on Web application injection vulnerabilities detection method based on pattern matching

Research on Web application injection vulnerabilities detect...

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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Ma, Qican Wu, Zehui Wang, Xinlei Wei, Qiang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9781510653290

Attackers can exploit vulnerabilities in web applications to commit malicious acts such as corrupting application functionality and Trojan horse implantation. For injection vulnerabilities in Web applications, existing methods are limited by the variety of programming languages and the difficulty in extracting semantic information to detect complex vulnerabilities. The paper proposes a pattern matching-based method for identifying injection vulnerabilities in Web applications, which transforms vulnerability identification into path matching in graphical databases by modifying the code property graph to enable it to handle more complex inter-functional relationships in Web applications. In this paper, we designed and implemented a prototype system, VulnFinder. By collecting 100 randomly selected Github high star open-source projects as the dataset for performance testing, we found 262 real vulnerabilities and conducted comparison experiments with static scanning tools RIPS and Cobra, VulnFinder far exceeded the comparison tools in terms of vulnerability determination accuracy, and in terms of the dataset VulnFinder was 94% accurate in determining vulnerabilities in the dataset. In scanning large projects, VulnFinder was approximately 21% more efficient than the comparable methodology tool RIPS. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Pattern matching

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IoT Device Multi-Classification Using Traffic Behavior Analysis 3

IoT Device Multi-Classification Using Traffic Behavior Analy...

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3rd International Conference on Internet of Things and Smart City, IoTSC 2023

作者： Shi, Jiaqi Liu, Tieming Zhang, Yuanyuan Ren, Huajuan Cyberspace Security Department State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Service Room Unit 32738 China

ISBN: (纸本)9781510666375

The management of Internet of Things (IoT) devices is becoming increasingly complex. One of the reasons is that IoT device manufacturers are different, and there are different degrees of heterogeneity in service, technology, protocol and other aspects. Accurate identification IoT devices connected to the organization network is an effective way to maintain the organization network security. In this paper, we propose a two-stage machine learning method to identify IoT devices by analyzing network traffic. This method uses the futures of the original traffic advance to classify the types of IoT devices. The method is tested on two public datasets, our method classified IoT devices with an accuracy rate of over 99%. © 2023 SPIE.

关键词： Machine learning

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Meta Transductive Linear Probing for Few-Shot Node Classification

Meta Transductive Linear Probing for Few-Shot Node Classific...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Mingjing Lan Ningbo Huang Gang Zhou Yi Xia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Graph few-shot learning aims to predict well by training with very few labeled data. Meta learning has been the most popular solution for few-shot learning problem. However, transductive linear probing shows that fine-tuning a simple linear classification head after a pretrained graph neural networks can outperforms most of the sophisticated-designed graph meta learning algorithms. Therefore, in the paper, we propose a meta transductive linear probing methods named Meta-TLP to incorporate the advantages of graph self-supervised and graph meta learning model. Specifically, the graph neural network is firstly pretrained with graph contrastive learning methods. Then we design an unsupervised meta training task construction methods to require meta tasks without relying on labeled data. Finally, we meta training the linear classification head on the meta training tasks to learn to fast adopt to novel classes. Experiment results show that our model can perform better than TLP on three real world datasets.

关键词： Training Metalearning Design methodology Data visualization Prediction algorithms Magnetic heads Data models Graph neural networks Pattern recognition Few shot learning

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Dual-axial self-attention network for text classification

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Science China(Information Sciences) 2021年第12期64卷 80-90页

作者： Xiaochuan ZHANG Xipeng QIU Jianmin PANG Fudong LIU Xingwei LI State Key Laboratory of Mathematical Engineering and Advanced Computing Shanghai Key Laboratory of Intelligent Information Processing Fudan University School of Computer Science Fudan University

Text classification is an important task in natural language processing and numerous studies aim to improve the accuracy and efficiency of text classification models. In this study, we propose an effective and efficient text classification model which is based on self-attention solely. The recently proposed multidimensional self-attention significantly improved the performance of self-attention. However, existing models suffer from two major limitations:(1) the previous multi-dimensional self-attention models are quite timeconsuming;(2) the dependencies of elements along the feature axis are not taken into account. To overcome these problems, in this paper, a much more computational efficient multi-dimensional self-attention model is proposed, and two parallel self-attention modules, called dual-axial self-attention, are applied to capture rich dependencies along the feature axis as well as the text axis. A text classification model is then *** experimental results on eight representative datasets show that the proposed text classification model can obtain state-of-the-art results and the proposed self-attention outperforms conventional self-attention models.

关键词： text classification dual-axial self-attention feature-axial dependency

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A GAN-based Method for Generating SQL Injection Attack Samples 10

A GAN-based Method for Generating SQL Injection Attack Sampl...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Lu, Dongzhe Fei, Jinlong Liu, Long Li, Zecun Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks. © 2022 IEEE.

关键词： Generative adversarial networks

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