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2015 4th International Conference on Computer, Mechatronics, Control and Electronic engineering (ICCMCEE 2015)

作者： Yan Lijing Shan Zheng Xu Xiaoyan Xue Fei The State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University

ISBN: (纸本)9781510813465

The context sensitivity is an important property in program analysis which can improve the analysis accuracy of the function call context of the source codes and helps to improve the abilities of the compiler optimization and verification procedures. The automata model based on the function call of the program source code is an effective method of modeling the behaviors of the current software, but this method does not consider the context sensitivity of the software function calls. To solve this problem, on the base of high-performance open source compiler Open64, we achieve a source code level context sensitivity exploration algorithm to analyze the relationships between the points of the function calls. Using the iterator, by the circulating way, the algorithm traverses the calling relationships between functions and obtains the context sensitive information analysis results. With this method, it can guide the source code modeling of using the single context sensitivity or the mixed context sensitivity models. In the benchmark sets SPEC2006 and NPB3.3.1, we verify the correctness of the algorithm, and the results show that the proposed algorithm can detect the context sensitivity of the source code level, and the hybrid context sensitivity modeling approach which we used, comparing with other modeling methods, has had a higher accuracy.

关键词： Behavior model Function call graph Context sensitivity Source code level Inter-procedure analysis

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Forgeability of Wang-Zhu-Feng-Yau’s Attribute-Based Signature with Policy-and-Endorsement Mechanism

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Journal of Computer Science & Technology 2013年第4期28卷 743-748页

作者：葛爱军黄欣沂陈成马传贵张锐 State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Fujian Provincial Key Laboratory of Network Security and Cryptology School of Mathematics and Computer ScienceFujian Normal University Institute of Software Chinese Academy of Sciences

Recently, Wang et al. presented a new construction of attribute-based signature with policy-and-endorsement mechanism. The existential unforgeability of their scheme was claimed to be based on the strong Diffie-Hellman assumption in the random oracle model. Unfortunately, by carefully revisiting the design and security proof of Wang et alfs scheme, we show that their scheme cannot provide unforgeability, namely, a forger, whose attributes do not satisfy a given signing predicate, can also generate valid signatures. We also point out the flaws in Wang et al.＇s proof.

关键词： attribute-based signature existential unforgeability policy-and-endorsement

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Analysis of cryptosystem recognition scheme based on Euclidean distance feature extraction in three machine learning classifiers 3

Analysis of cryptosystem recognition scheme based on Euclide...

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2019 3rd International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2019

作者： Zhao, Yaqun Fan, Sijie State Key Laboratory of Mathematical Engineering and Advanced Computing 450001 China State Key Laboratory of Cryptography and Science 450001 China

In reality, many cryptographic analysis techniques are based on a specific cryptographic system or a large number of encrypted ciphertext. The identification and detection of cryptographic system is of great significance for evaluating the security of the algorithm and guiding the design and improvement of the algorithm. In this paper, we transcode each character in ciphertext into a decimal number, construct these numbers into one-dimensional arrays, and obtain the Euclidean distance between these one-dimensional arrays. Then we use these distances as features and input them into three machine learning classifiers: random forest, logistic regression and support vector machine to recognize cryptosystem and compare their recognition accuracy. The subjects include 8 common block ciphers (DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish, Camellia-128). The experimental results show that using the feature extraction scheme not only shortens the experimental time, reduces the computational cost, but also improves the recognition accuracy of eight typical block cipher algorithms. The classification accuracy of the ECB mode in the random forest classifier is 75%, which is higher than the existing published literature experimental results. The classification accuracy rate of CBC mode is higher than 13.5%, which is higher than the accuracy of random classification. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Programming Logic Modeling and Cross-Program Defect Detection Method for Object-Oriented Code

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Computers, Materials & Continua 2020年第7期64卷 273-295页

作者： Yan Liu Wenyuan Fang Qiang Wei Yuan Zhao Liang Wang State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China The School of Computing Newcastle UniversityNewcastle upon TyneNE45TGUK

Code defects can lead to software vulnerability and even produce vulnerability *** research shows that the code detection technology with text analysis can judge whether object-oriented code files are defective to some ***,these detection techniques are mainly based on text features and have weak detection capabilities across *** with the uncertainty of the code and text caused by the developer’s personalization,the programming language has a stricter logical specification,which reflects the rules and requirements of the language itself and the developer’s potential way of *** article replaces text analysis with programming logic modeling,breaks through the limitation of code text analysis solely relying on the probability of sentence/word occurrence in the code,and proposes an object-oriented language programming logic construction method based on method constraint relationships,selecting features through hypothesis testing ideas,and construct support vector machine classifier to detect class files with defects and reduce the impact of personalized programming on detection *** the experiment,some representative Android applications were selected to test and compare the proposed *** terms of the accuracy of code defect detection,through cross validation,the proposed method and the existing leading methods all reach an average of more than 90%.In the aspect of cross program detection,the method proposed in this paper is superior to the other two leading methods in accuracy,recall and F1 value.

关键词： Method constraint relationship programming logic code defect hypothesis test

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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Polytopic Attack on Round-Reduced Simon32/64 Using Deep Learning 16th

Polytopic Attack on Round-Reduced Simon32/64 Using Deep Lear...

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16th International Conference on Information Security and Cryptology, Inscrypt 2020

作者： Su, Heng-Chuan Zhu, Xuan-Yong Ming, Duan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9783030718510

In CRYPTO 2019, Gohr uses the residual network technology of artificial intelligence to build a differential distinguisher, and attacks the reduced-round Speck32/64. We tried this method to recover the keys for ten-round Simon32/64. In this paper, we have three innovations. First, we construct polytope neural network distinguisher. On eight-round Simon32/64, polytope neural network distinguisher could increase the success rate of three neural network distinguishers with 0.76 success rate to 0.92. Second, we propose an attack on Simon32/64 based on the combination of the probability of differential path and polytope neural network distinguisher. This method can only increase the computational complexity of the chosen data as the number of rounds increases. Nine-round polytope neural network distinguisher is used to filter out data, whether it is what we want. Eight-round neural distinguisher is used to recover the final round key. The computational complexity of key recovery on the final key of eleven-round Simon32/64 is 233.4. Third, we propose an attack called Bayesian key Research with Error. With this attack, the computational complexity of key recovery on the final key of eleven-round Simon32/64 is 230.9. In our paper, the main idea is combining polytope differences with neural networks. By constructing polytope differential neural network distinguisher, we make a key recovery attack. In order to increase the number of rounds, we first used brute force attack and then proposed Bayesian key Research with Error. We think this idea can be applied to many cryptographic algorithms. © 2021, Springer Nature Switzerland AG.

关键词： Computational complexity

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Network traffic classification based on multi-head attention and deep metric learning 8

Network traffic classification based on multi-head attention...

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8th International Conference on Electronic Technology and Information Science, ICETIS 2023

作者： Lv, Zhuo Lu, Bin Li, Xue Qi, Zan State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Information Engineering University Zhengzhou China

ISBN: (纸本)9781510666535

Network traffic classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories. © 2023 SPIE.

关键词： Classification (of information)

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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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Reducing DRAM refreshing in an error correction manner

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Science China Chemistry 2015年第12期58卷 186-199页

作者： ZHU DanFeng WANG Rui WEI YanJiang QIAN DePei School of Computer Science and Engineering Beihang University State Key Laboratory of Mathematical Engineering and Advanced Computing

Dynamic random access memory(DRAM) is facing the challenge of technology scaling. The decreasing feature size makes it harder to make DRAM cells which can keep the current data-holding time. When DRAM cells cannot hold data for a long time, DRAM chips need a more frequent refreshing operation. Therefore, in the near future, time and energy cost on DRAM refreshing will be no longer trivial. In this paper, we propose DRAM Error Correction Pointer(ECP), an error-correction-manner framework, to reduce DRAM refreshes without data loss. We exploit the non-uniform feature of DRAM cells with respect to the data retention time. Compared with the conventional refreshing mechanisms, which refresh DRAM chips by the retention time of the leakiest cells, we refresh the chips much fewer times, and treat the not-in-time refreshed cells as fault elements. We use the structure of ECP as a fault tolerant element. By recording the data which are supposed to be written into the leaky cells in our DRAM-ECP structures, DRAM-ECP can significantly decrease refreshing frequency. When these data are to be read out, DRAM-ECP retrieves the data stored in ECPs and covers them to the corresponding position in the data row. Our experiments show that DRAM-ECP can reduce over 70%refreshing operations than the current refreshing mechanism and also get significant energy saving.

关键词： DRAM refreshing error correction DRAM-ECP counting bloom filter retention time

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