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

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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Code Level Context Sensitivity Exploration Algorithm

Code Level Context Sensitivity Exploration Algorithm

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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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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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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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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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On the complete weight distributions of quantum error-correcting codes

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Chinese Physics B 2023年第5期32卷 272-287页

作者：杜超马智熊茂胜 State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenghzhou 450001China Henan Key Laboratory of Network Cryptography Technology Zhenghzhou 450001China Department of Mathematics Hong Kong University of Science and TechnologyHong KongChina

In a recent paper, Hu et al. defined the complete weight distributions of quantum codes and proved the Mac Williams identities, and as applications they showed how such weight distributions may be used to obtain the singleton-type and hamming-type bounds for asymmetric quantum codes. In this paper we extend their study much further and obtain several new results concerning the complete weight distributions of quantum codes and applications. In particular, we provide a new proof of the Mac Williams identities of the complete weight distributions of quantum codes. We obtain new information about the weight distributions of quantum MDS codes and the double weight distribution of asymmetric quantum MDS codes. We get new identities involving the complete weight distributions of two different quantum codes. We estimate the complete weight distributions of quantum codes under special conditions and show that quantum BCH codes by the Hermitian construction from primitive, narrow-sense BCH codes satisfy these conditions and hence these estimate applies.

关键词： quantum codes complete weight distributions Mac Williams identities BCH codes

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VFDETECT: A Vulnerable Code Clone Detection System Based on Vulnerability Fingerprint 3

VFDETECT: A Vulnerable Code Clone Detection System Based on ...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Zhen Liu Qiang Wei Yan Cao State Key Laboratory of Mathematical Engineering and Advanced Computing China National Digital Switching System Engineering and Technological Research Center

Vulnerable code reuse in open source software is a serious threat to software ***,the existing highefficiency methods for vulnerable code clone detection have a large number of false-nega

ISBN: (纸本)9781509053643;9781509053636

Vulnerable code reuse in open source software is a serious threat to software ***,the existing highefficiency methods for vulnerable code clone detection have a large number of false-nega

关键词： Cloning Fingerprint recognition Feature extraction Linux Kernel Data structures

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Optimizing execution for pipelined-based distributed deep learning in a heterogeneously networked GPU cluster

Optimizing execution for pipelined-based distributed deep le...

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作者： Zhang, Jinghui Zhan, Jun Li, Jiange Jin, Jiahui Qian, Lei School of Computer Science and Engineering Southeast University Nanjing China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China

Exorbitant resources (computing and memory) are required to train a deep neural network (DNN). Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. This approach has its detriments, though;on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter-GPU's communications during model training, and multi-GPU systems lead to complex connectivity. Workload schedulers then end up having to consider hardware topology and requirements for workload communication, in hopes of allocating GPU resources to optimize execution time and improve usage in a heterogeneous environment. On the other hand, the high memory requirements to train a DNN model make running the training processes on GPUs onerous. To contend with this, we introduce two execution optimization methods based on pipeline-hybrid parallelism (using both data and model parallelism) in a GPU cluster with heterogeneous networking. First, we propose a model partition algorithm that accelerates pipeline-hybrid parallelism training between heterogeneously network-connected GPUs. Second, we introduce a cost-balanced recomputing algorithm to reduce memory usage in the pipeline mode. Experiments show that our solution (Pipe-Torch) averages a speedup of 1.4× compared with data parallelism, and reduces the memory footprint while maintaining pipelined load-balanced training. © 2020 John Wiley & Sons Ltd

关键词： Pipelines

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