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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Li, Chengxuan Yang, Zhou Xiao, Yang Liu, Haozhao Zhang, Yuning Pei, Qingqi Xidian University State Key Laboratory of Integrated Services Networks Xi'an China School of Telecommunications Engineering Xidian University Xi'an China School of Cyber Engineering Xidian University Xi'an China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an China

ISBN: (纸本)9798350327380

Deep learning-based image recognition technology has significantly advanced the development of modern industrial intelligence. However, the issue of image adversarial examples that follows has gradually garnered the attention of researchers. By injecting adversarial disturbances that are difficult for humans to detect into the image, the deep learning model generates incorrect results, severely impacting the security of deep learning applications. To address this problem and improve the accuracy and robustness of deep learning image recognition models, a combined adversarial examples detection method based on residual learning and difference assessment has been proposed. To start, an image denoising module, based on residual learning and named ANDNet, is designed. This method incorporates a depth separable convolution structure and conducts step processing on the channel number of the hidden layer in ANDNet, which significantly reduces the model's memory consumption and improves its computational efficiency. In addition, the difference assessment involves computing the l1 norm distance between the original image and the denoised image softmax output of the classification model, to measure the disparity between the input image before and after denoising. The obtained detection threshold from the training dataset is integrated with this difference evaluation to accomplish the task of testing the input image for adversarial detection. Both theoretical analysis and experimental results confirm the effectiveness of the ANDNet noise reduction network, as well as the efficacy of the adversarial examples detection scheme in identifying adversarial examples. The model exhibits exceptional performance in terms of detection accuracy and F1 value. © 2023 IEEE.

关键词： Image recognition

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A Modeling Method of Cyberspace Security Structure Based on Layer-Level Division 5

A Modeling Method of Cyberspace Security Structure Based on ...

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5th IEEE International Conference on Computer and Communication engineering Technology, CCET 2022

作者： Zhu, Yuwen Yu, Lei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Security Institute of Information Engineering Beijing100000 China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

As the cyberspace structure becomes more and more complex, the problems of dynamic network space topology, complex composition structure, large spanning space scale, and a high degree of self-organization are becoming more and more important. In this paper, we model the cyberspace elements and their dependencies by combining the knowledge of graph theory. Layer adopts a network space modeling method combining virtual and real, and level adopts a spatial iteration method. Combining the layer-level models into one, this paper proposes a fast modeling method for cyberspace security structure model with network connection relationship, hierarchical relationship, and vulnerability information as input. This method can not only clearly express the individual vulnerability constraints in the network space, but also clearly express the hierarchical relationship of the complex dependencies of network individuals. For independent network elements or independent network element groups, it has flexibility and can greatly reduce the computational complexity in later applications. © 2022 IEEE.

关键词： Graph theory

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Nitrogen removal from summer to winter in a field pilot-scale multistage constructed wetland-pond system

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Journal of Environmental Sciences 2022年第1期34卷 249-262页

作者： Tao Wang Liping Xiao Hongbin Lu Shaoyong Lu Jiaxin Li Xiaochun Guo Xiaoliang Zhao National Engineering Laboratory for Lake Pollution Control and Ecological Restoration State Environmental Protection Scientific Observation and Research Station for Lake Dongtinghu(SEPSORSLD)State Environmental Protection Key Laboratory for Lake Pollution ControlState Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijing 100012China School of Environmental and Municipal Engineering Qingdao University of TechnologyQingdao 266033China College of Environmental Science and Engineering Liaoning Technical UniversityFuxin 123000China College of Water Sciences Beijing Normal UniversityBeying 100875China

A pilot-scale multistage constructed wetland-pond(MCWP)system with a"pre-ecological oxidation pond,two-stage horizontal subsurface flow constructed wetland(HSCW)and surface flow constructed wetland(SFCW)as the core and postsubmerged plant pond"as the process was used to treat actual polluted river water in the field,and the variation in nitrogen removal from summer to winter was *** results showed that the average total nitrogen(TN)removal efficiency in the MCWP was approximately 40.74%.The significant positive correlation between the daily highest temperature and the TN removal efficiency of the whole system was fitted with a nonlinear curve(R^(2)=0.7192).The TN removal load rate in the HSCWs was 2.7–3.7 times that in the *** SFCW,which had high-density plants(35 plants/m^(2)),increased the proportion of nitrogen removed by plant harvesting and microbial *** TN transformed by Iris pseudacorus *** for 54.53%in the ***,bacteria completed the nitrogen cycle in the SFCW through a variety of nitrogen removal *** research not only investigated the TN removal performance in an MCWP system but also made it possible to predict the TN removal efficiency according to the daily highest temperature from summer to winter in the field.

关键词： Multistage constructed wetland-pond Total nitrogen Air temperature change Emergent plants Polluted surface water

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Exploitability Analysis of Public Component Library Vulnerabilities Based on Taint Analysis 7

Exploitability Analysis of Public Component Library Vulnerab...

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7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yuan, Huijie Wang, Yunchao Zong, Guoxiao Lv, Zhuo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Grid Electric Power Research Institute Zhengzhou China

ISBN: (纸本)9781665478571

The reuse of public component libraries has contributed to the field of computer science because of its low implementation time and high production efficiency. However, the vulnerabilities in libraries can be more serious than those in real-world software because they can affect various software which uses these libraries. Therefore, the technologies of vulnerability mining of public component libraries have received extensive attention, such as fuzzing. But the number of crashes generated by fuzzing is huge, and only an exceedingly small number of public component library vulnerabilities are exploitable in real-world software. To solve this problem, we use taint analysis techniques to analyze the exploitability of library vulnerabilities in real-world software. We first use Pin binary instrumentation technology to instrument the consumer program, analyze the process of calling the library in the consumer program through the taint analysis, and convert the extracted execution path and parameter information into an adjacency matrix. Then we analyze the execution path and crash scene of the crash file, convert the exploitability analysis into path reachability analysis, and determine whether the crash can reach the vulnerable pointer of the software through reachability. Finally, we divide the library vulnerabilities into three levels: directly exploitable, indirectly exploitable, and unexploitable. We design and implement a prototype tool, LibExp-T, to analyze nine public component libraries and four real-world software containing multiple attack surfaces such as images, audio, video, fonts, etc. And we compare them with automatic exploit generation tools CRAX and REX. The results show that LibExp-T can effectively verify the exploitability of component library vulnerabilities in real-world software with low overhead. © 2022 IEEE.

关键词： Libraries

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Graph Low-Rank Non-Negative Matrix Factorization with Auto-Encoders for Fault Detection 14

Graph Low-Rank Non-Negative Matrix Factorization with Auto-E...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Liu, Yabing Yu, Shanshan Guo, Wei Leung, Man-Fai School of Electronic and Information Engineering Southwest University Chongqing China South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing Wuhan430074 China School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9798350353334

Fault detection is the process of detecting and diagnosing faults or abnormalities in a system by analyzing its operational data. However, with the complexity of modern industrial processes, some faults are difficult to be detected in a timely manner due to various factors such as noise and data nonlinearity. Therefore, data-driven Fault Detection (FD) has become a widely used method to detect abnormal events in functional modules. Non-negative Matrix Factorization (NMF), as an efficient dimensionality reduction technique, has not had potential applications in fault detection (FD) thoroughly explored. In order to improve the FD methods based on NMF, we have developed a new approach, named graph low-rank non-negative matrix Factorization with auto-encoders (GLNMFA). GLN-MFA integrates the Laplacian operator effectively identifies t he l ocal s tructure a mong d ata p oints, enhancing the performance of dimensionality reduction algorithms. It also introduces the nuclear norm to find a low-rank approximation to the original matrix, thereby constraining sparsity. Additionally, auto-encoders are incorporated to learn a low-dimensional representation of the data and extract key features, which are subsequently applied for fault detection purposes. We employ an optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) to optimize this model. Two test statistics T2 (Hotelling's T-squared), SPE (Squared Prediction Error) are used to evaluate detection efficiency. K ernel Density Estimation (KDE) are used to estimate control limits for fault detection. The effectiveness of GLNMFA is validated on two benchmark datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Zokfuzz: Detection of Web Vulnerabilities via Fuzzing 2

Zokfuzz: Detection of Web Vulnerabilities via Fuzzing

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2nd International Conference on Consumer Electronics and Computer engineering, ICCECE 2022

作者： Zhang, Hao Dong, Weiyu Jiang, Liehui School of Cyber Science and Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665408868

Fuzzing is one of the mainstream web application automated vulnerability detection methods. Because of its black box characteristics, it can be used to detect vulnerabilities without knowing the source code of the target application, so it is widely used. However, traditional web application fuzzing methods have the disadvantages of limited test cases, slow execution process, and low efficiency. In order to solve these problems, some scholars introduced genetic algorithms in the fuzzing process. However, after the test cases are entered, it is difficult to automatically detect the state of the web application, which will lead to deviations in the evolution direction of the population and the high aggressiveness of the test cases. This paper proposes a web application fuzzing script, which can realize automatic monitoring of web application status changes, and at the same time more accurately judge whether the test case is offensive. On this basis, an improved mutation method is proposed to enable the population to quickly evolve more aggressive test cases. Experimental results show that ZokFuzz can detect more web vulnerabilities compared to X-Ray and Burp Suite. © 2022 IEEE.

关键词： Genetic algorithms

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A Honeywords Generation Method Based on Deep Learning and Rule-Based Password Attack 4th

A Honeywords Generation Method Based on Deep Learning and Ru...

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4th EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2021

作者： Yang, Kunyu Hu, Xuexian Zhang, Qihui Wei, Jianghong Liu, Wenfen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030967901

Honeywords is a simple and efficient method that can help the authentication server to detect password leaks. The indistinguishability between generated honeywords and real passwords is the key to the honeywords generation methods. However, the current honeywords generation methods are difficult to achieve that and are vulnerable to the Top-PW attack. In order to improve the security of the honeywords generation method, this paper combines the deep learning and rule-based password attacks to propose a new honeywords generation method called GHDR. The method first builds a deep learning model to learn whether the rules used in the rule-based password attack are effective for different passwords. When a user sets a password, effective rules will be selected by the model according to the entered password, and then these selected rules will be used to transform the user’s password to generate honeywords. Experimental results show that the proposed honeywords generation method can better resist Top-PW attacks than the state-of-the-art methods. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Authentication

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A riemannian exponential augmented lagrangian method for computing the projection robust Wasserstein distance 23

A riemannian exponential augmented lagrangian method for com...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bo Jiang Ya-Feng Liu Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Projection robust Wasserstein (PRW) distance is recently proposed to efficiently mitigate the curse of dimensionality in the classical Wasserstein distance. In this paper, by equivalently reformulating the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints, we propose a Riemannian exponential augmented Lagrangian method (REALM) for solving this problem. Compared with the existing Riemannian exponential penalty-based approaches, REALM can potentially avoid too small penalty parameters and exhibit more stable numerical performance. To solve the subproblems in REALM efficiently, we design an inexact Riemannian Barzilai-Borwein method with Sinkhorn iteration (iRBBS), which selects the stepsizes adaptively rather than tuning the step-sizes in efforts as done in the existing methods. We show that iRBBS can return an ε-stationary point of the original PRW distance problem within O(ε-3) iterations, which matches the best known iteration complexity result. Extensive numerical results demonstrate that our proposed methods outperform the state-of-the-art solvers for computing the PRW distance.

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A Highly Secure C2C-PAKE Cross-domain Protocol

A Highly Secure C2C-PAKE Cross-domain Protocol

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2021 International Conference on Network Communication and Information Security, ICNCIS 2021

作者： Wang, Xiangyang Gu, Chunxiang Lu, Siqi Chen, Xi Henan Key Laboratory of Network Cryptography Technology Henan Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China

ISBN: (数字)9781510653283

ISBN: (纸本)9781510653276

This paper studies the password-based strong authentication key exchange protocol in the cross-domain scenario, and gives an end-to-end highly secure C2C-PAKE protocol, in which the server shares a password with the client and also has a pair of private/public keys. In contrast, the client has the shared password and the server's public-key. Specifically, we combine the ECQV implicit certificate scheme with the ECDH key exchange protocol to give a highly secure cross-domain protocol. Considering the application of the ECQV implicit certificate scheme in the Internet of Things (IoT), we give an analysis the possibility of the proposed protocol applied in the cross-domain scenario of the IoT. Finally, based on some common attacks, we show a security analysis of our protocol, which achieves perfect forward security and resists dictionary attacks, KCI attacks, replay attacks, Insider-Assisted attacks, and so on. © 2022 SPIE

关键词： Computer security Network security Cryptography

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Guest Editorial Enabling Technologies and Systems for Industry 5.0: From Foundation Models to Foundation Intelligence

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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