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Zokfuzz: Detection of Web Vulnerabilities via Fuzzing 2

Zokfuzz: Detection of Web Vulnerabilities via Fuzzing

引用

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...

引用

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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Research on Fuzzing Technology for JavaScript Engines 21

Research on Fuzzing Technology for JavaScript Engines

引用

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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An Intrusion Detection Feature Selection Method Based on Improved Mutual Information

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

引用

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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An Improved key Mismatch Attack on Kyber

An Improved Key Mismatch Attack on Kyber

引用

Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Yaru Wang Haodong Jiang Zhi Ma State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Recently, American National Institute of Standards and Technology (NIST) announced Kyber as the first KEM candidate to be standardized. The security of Kyber is based on the modular learning with errors (MLWE) problem, which achieves excellent efficiency and size. This work proposes an improved key mismatch on Kyber, which can reduce the number of queries required to recover the secret key. We first transform the problem of finding a certain parameter of ciphertexts into a quantum ordered search problem. Then we give the procedure of finding the value of a parameter in the ciphertexts by the quantum method. Finally, we instantiate this attack method on Kyber512, Kyber768 and Kyber1024. Compared with the existing attack algorithm, our improved attack reduces the number of queries for Kyber512, Kyber768 and Kyber1024 by 63%, 59% and 45%, respectively.

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A Survey of Security Analysis on Industrial Control Devices via Rehosting

A Survey of Security Analysis on Industrial Control Devices ...

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Houzhi Liu Rongkuan Ma Qiang Wei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

At present, the network security of embedded devices has received more and more attention. However, the existing dynamic analysis tools can’t performance in embedded devices as common softwares. The main reason is that the internal state information of the embedded device can not be directly obtained, and the debugging interface usually shields the device after the manufacturer produces it. Thus, this paper introduces rehosting techniques for embedded systems and the differences between each way. Then we talk about the Deficiencies and future works in emulate execution.

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Critical Variable Guided Mutation for Directed Greybox Fuzzing

Critical Variable Guided Mutation for Directed Greybox Fuzzi...

引用

Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Yubo He Long Liu The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Directed greybox fuzzing directs fuzzers to specified code areas and has gained great achievements in 1-day vulnerability detection. However, existing directed graybox fuzzers fail to generate the crash sample even if they found a testcase reaching the target site. There are mainly two questions that affect the effectiveness of directed greybox fuzzing: basic block-level target is coarse enough for 1-day vulnerability detection and the fuzzers follow a specific rule to select operators regardless of the vulnerability itself. This paper points out that only a few vulnerability-related variables are related to the vulnerability triggering. Based on the vulnerability-related variables, this paper proposes critical variable guided mutation, a mutation scheduling method to enhance the crash reproduction capability of directed greybox fuzzing. We implemented a prototype MDGF based on the critical variable guided mutation and evaluate it on real world programs. Evaluation of MDGF on various real-world programs showed that MDGF found vulnerabilities faster than the mainstream directed greybox fuzzers. The experimental results showed that the speed of MDGF is 6.18 times faster than that of AFLGo and 1.40 times faster than Beacon, and MDGF can find 1.71x more bugs than AFLGo.

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Crop Enclave Interface for SGX Programs

Crop Enclave Interface for SGX Programs

引用

IEEE International Conference on Joint Cloud computing (JCC)

作者： Yaqi Fan Zhifeng Chen Qingbao Li Wenbo Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Existing memory attacks against SGX use the enclave interface, such as ECALLs and OCALLs, to inject malicious data into the enclave’s trusted memory to trigger memory corruption vulnerabilities therein. Therefore, enclave interface security becomes a key issue in defending against such attacks. However, a comprehensive static analysis of source SGX programs is currently lacking to obtain sufficient a priori knowledge to provide effective runtime interface protection for the enclave. In view of this, we identify 8 types of unsafe input data of enclave and design a new interface cropping method, SGXCrop. This method extracts critical interface information from source SGX programs, including ECALLs in use and unsafe input data, which are cropped at runtime of SGX programs. Tests in real SGX environment verify that the proposed method can effectively crop illegal ECALLs and unsafe input data.

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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...

引用

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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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

引用

International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Xin Zhou Jianmin Pang Chunyan Zhang Feng Yue Junchao Wang Guangming Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities.

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