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

作者： Li, Chenwei Pan, Mengyuan Yang, Bo Zhang, Hengwei Henan Key Laboratory of Information Security State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Guizhou University School of Chemistry and Chemical Engineering Guiyang China

ISBN: (纸本)9798350327076

Convolutional neural networks are widely used in computer vision and image processing. However, when the original input is added with manually imperceptible perturbations, these deep network models mostly tend to output incorrect predictions. The vulnerability of these models poses great threat to intelligent applications, and these manually imperceptible perturbations are called adversarial examples. Current baseline methods have achieved considerable white-box attack success rate, but black-box rate remains to be improved. To boost the adversarial generalization, ensemble models method is introduced to the process of generating adversarial examples. This paper proposes multiple ensemble strategies with baseline attack methods based on existing ensemble strategy used by former methods. Experiment on ImageNet dataset empirically verifies the optimal ensemble strategy on boosting adversarial generalization. © 2023 IEEE.

关键词： Image classification

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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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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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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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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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Rotation Model Enhancement for Adversarial Attack

Rotation Model Enhancement for Adversarial Attack

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhang, Hengwei Li, Zheming Liu, Hao Yang, Bo Li, Chenwei Wang, Jindong State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Key Laboratory of Information Security Zhengzhou China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Current white-box attack to deep neural networks have achieved considerable success, but not for black-box attack. The main reason is poor transferability, as the adversarial examples are crafted with single deep neural networks model, and excessively depend on that model. To address that problem, we propose a rotation model enhancement algorithm to craft adversarial examples. We improve rotation method in model enhancement. This algorithm constructs a possibility model to randomly rotate original images, and generates multiple transformed images. Therefore, we craft adversarial examples with single model, and boost attack on multiple models, which demonstrate considerable transferability and success rate for black-box attack. The simulation indicates the algorithm boost black-box attack with a 89.2% success rate. © 2022 IEEE.

关键词： Deep neural networks

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Misleading Image Classification with Multi-shearing and Random Padding 6

Misleading Image Classification with Multi-shearing and Rand...

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Li, Chenwei Li, Zheming Yang, Bo Zhang, Hengwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Key Laboratory of Information Security Zhengzhou China

ISBN: (纸本)9781450397148

Neural networks are vulnerable when input data is applied with human-imperceptible perturbations, which is called adversarial examples. When used in image classification models, adversarial examples mislead neural netwoks to classify images with wrong labels, posing great threat to network security. White-box attack has achieved considerable success rate, for the model structure is already known. But black-box attack remains to be improved, so as to the transferability. We refer to the model augmentation method in network training process, and apply to generating adversarial examples to reduce overfitting. Consulting fundamental methods in adversarial examples, we propose a multi-cropping transformation method to alleviate overfitting and enhance transferability. Firstly, referring to data augmentation, we multi-crop original images in every iteration with random possibilities in adversarial exaples generating process. Secondly, the gradient of model loss function is calculated, and the perturbations are added to original images. Finally, we generate adversarial examples with iterative perturbations. The validation of our method is verified on single models and ensemble models, and the transferability is improved, compared to other fundamental methods. © 2022 Association for computing Machinery.

关键词： Shearing

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