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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 New Sixth-Order WENO Scheme for Solving Hyperbolic Conservation Laws

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Communications on Applied Mathematics and Computation 2023年第1期5卷 3-30页

作者： Kunlei Zhao Yulong Du Li Yuan State Key Laboratory of Scientific and Engineering Computing(LSEC)and Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences Beihang UniversityBeijing 100191China

In this paper,we develop a new sixth-order WENO scheme by adopting a convex combina-tion of a sixth-order global reconstruction and four low-order local *** the classical WENO schemes,the associated linear weights of the new scheme can be any positive numbers with the only requirement that their sum equals ***,a very simple smoothness indicator for the global stencil is *** new scheme can achieve sixth-order accuracy in smooth *** tests in some one-and two-dimensional bench-mark problems show that the new scheme has a little bit higher resolution compared with the recently developed sixth-order WENO-Z6 scheme,and it is more efficient than the classical fifth-order WENO-JS5 scheme and the recently developed sixth-order WENO6-S scheme.

关键词： Global smoothness indicator Linear weights Sixth-order accuracy WENO

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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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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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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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Stepwise DOA Estimation for Coherent and Uncorrelated Mixed Signals on Coprime Array 5

Stepwise DOA Estimation for Coherent and Uncorrelated Mixed ...

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5th IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Jiang, Heng Li, Lichun Zhang, Hailong Pan, Hongyi Li, Xue College of Information System Engineering Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665479677

The coprime array constructs virtual array to obtain higher degrees of freedom (DOF), but the premise of doing so is that the signals are independent of each other. Once there are coherent signals, the signal model of virtual array is destroyed and the method fails. To solve these problems, we propose the stepwise estimation of the Direction of Arrival (DOA) for a mixture of coherent and uncorrelated signals. Firstly, the symmetry of the two coprime subarrays is used to eliminate the uncorrelated signals, and combined with the properties of prime numbers, the coherent source DOA is jointly estimated. The obtained coherent source DOA was combined with the optimization algorithm to remove the coherent signal in the mixed signal. Finally, the virtual domain signal processing method was used to estimate the independent source DOA. This method can effectively reduce the influence of coherent signals on the accuracy of DOA estimation without loss of degrees of freedom. In this paper, the feasibility of the proposed method is proved by theoretical derivation, and the effectiveness of the method is also verified by experimental simulations. © 2022 IEEE.

关键词： Direction of arrival

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Research on Fuzzing Technology for JavaScript Engines 21

Research on Fuzzing Technology for JavaScript Engines

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Tian, Ye Qin, Xiaojun Gan, Shuitao State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng zhou Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781450389853

JavaScript engine is the core component of web browsers, whose security issues are one of the critical aspects of the overall Web Eco-Security. Fuzzing technology, as an efficient software testing approach, has been widely applied to detecting vulnerabilities in different JavaScript engines, which is a security research hotspot at present. Based on systematical dissection of existing fuzzing methods, this paper reviews the development and technical ideas of JavaScript Engine Fuzzing combined with taxonomy, proposes a general framework of JavaScript Engine Fuzzing and analyzes the key techniques involved. Finally, we discuss the core issues that restrict efficiency in current research and present an outlook on the future trends of JavaScript Engine Fuzzing.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Engines

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Adversarial Example Attacks Against Intelligent Malware Detection: A Survey 4

Adversarial Example Attacks Against Intelligent Malware Dete...

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4th International Conference on Applied Machine Learning, ICAML 2022

作者： Qi, Xuyan Tang, Yonghe Wang, Huanwei Liu, Tieming Jing, Jing State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665462655

With the advancement of information technology and the increasing prevalence of the internet, the number of malware has experienced an exponential, and the security threat to users' property and privacy is becoming more serious. With the great success of artificial intelligence especially deep learning technology in fields such as computer vision and speech processing, deep learning are gradually introduced into the field of malware detection, to process large amounts of malware more efficiently. However, in recent years, researchers have discovered that the deep learning model itself also has security risks. While malware detection based on deep learning brings convenience and technical progress, it also introduces some new threats, such as adversarial examples. This paper investigates adversarial example attacks against deep learning-based malware detectors. First, We reviewed research on malware detection techniques. Then we analyze the works related to adversarial example attacks against intelligent malware detectors. Finally, we discuss the challenges and prospects in future research. © 2022 IEEE.

关键词： Malware

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An Intrusion Detection Feature Selection Method Based on Improved Mutual Information

An Intrusion Detection Feature Selection Method Based on Imp...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Zan Qi Jinlong Fei Jue Wang Xue Li State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Feature selection methods for classification are crucial for intrusion detection techniques using machine learning. High-dimensional features in intrusion detection data affect computational complexity, consume more used resources and more time for data analysis, and the irrelevant and redundant features among them often hinder the performance of classifiers and mislead the classification task. Therefore, it is challenging to select more relevant features from intrusion detection data containing many such features. In this paper, we propose an efficient feature selection algorithm that first considers the correlation between features and the redundancy of pairs of features with respect to class labels based on an improved Pearson correlation coefficient, and later improves the evaluation function based on conditional mutual information to obtain a final subset of features with the goal of improving the classification rate and accuracy. The proposed feature selection method based on improved conditional mutual information is compared with three existing feature selection methods on the frequently studied public benchmark intrusion detection dataset NSL-KDD. The experimental results show that the features selected by the proposed method in this paper lead to a significant reduction in execution time while resulting in higher classification accuracy.

关键词： Correlation coefficient Correlation Redundancy Intrusion detection Machine learning Telecommunication traffic Feature extraction

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Research on Cross-site Scripting Attack Detection Technology Based on Few-shot Learning

Research on Cross-site Scripting Attack Detection Technology...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Dongzhe Lu Long Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

With the intensification of informatization and mobility, various web security threats are emerging. Cross-site scripting (XSS) attack is the most common type of web attack. Most traditional detection methods have been difficult to adapt to the existing confusion variants of XSS attacks. In this paper, we extract features based on big data collected from 2017 to 2022. In order to improve the XSS detection effect of detection tools, we build machine learning models based on more than 210,000 positive and negative samples, among which CNN has the best performance. Furthermore, we propose a new algorithm that improves the traditional virtual sample generation technology based on prior knowledge in order to improve the generalization of the models. Experimental results show that in most cases, the performance of the algorithm in this paper is better than other VSG methods, and the ability to detect and discover unknown attacks is improved to a certain extent.

关键词： Machine learning algorithms Automation Cross-site scripting Perturbation methods Neural networks Machine learning Big Data

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