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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xia, Bing Ge, Yunxiang Yang, Ruinan Yin, Jiabin Pang, Jianmin Tang, Chongjun Dept.ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Dept.ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Dept.ZhongYuan University of Technology Zhengzhou China Dept.Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350312461

Conducting binary function naming helps reverse engineers understand the internal workings of the code and perform malicious code analysis without accessing the source code. However, the loss of debugging information poses the challenge of insufficient high-level semantic information description for stripping binary code function naming. Meanwhile, the existing binary function naming scheme has one function label for only one sample. The long-tail effect of function labels for a single sample makes the machine learning-based prediction models face the challenge. To obtain a function correlation label and improve the propensity score of uncommon tail labels, we propose a multi-label learning-based binary function naming model BContext2Name. This model automatically generates relevant labels for binary function naming by function context information with the help of PfastreXML model. The experimental results show that BContext2Name can enrich function labels and alleviate the long-tail effect that exists for a single sample class. To obtain high-level semantics of binary functions, we align pseudocode and basic blocks based on disassembly and decompilation, identify concrete or abstract values of API parameters by variable tracking, and construct API-enhanced control flow graphs. Finally, a seq2seq neural network translation model with attention mechanism is constructed between function multi-label learning and enhanced control flow graphs. Experiments on the dataset reveal that the F1 values of the BContext2Name model improve by 3.55% and 15.23% over the state-of-the-art XFL and Nero, respectively. This indicates that function multi-label learning can provide accurate labels for binary functions and can help reverse analysts understand the inner working mechanism of binary code. Code and data for this evaluation are available at https://***/CSecurityZhongYuan/BContext2Name. © 2023 IEEE.

关键词： Graphic methods

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A Firmware Vulnerability Detection Method Based on Feature Filtering 21

A Firmware Vulnerability Detection Method Based on Feature F...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Bing Tang, Chongjun Liu, Wenbo Chu, Shihao Dong, Yu ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Dept Zhengzhou China ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Dept Zhengzhou China ZhongYuan University of Technology Dept Zhengzhou China

ISBN: (纸本)9798350329223

Open source code reuse and code cross-platform deployment accelerate the spread of software vulnerabilities, and pose challenges for accurate detection of cross-platform vulnerabilities. The binary vulnerability similarity detection method based on machine learning lacks a fine-grained vulnerability similarity feature filtering mechanism, and cannot solve cross-function vulnerabilities triggered by data dependencies, resulting in a high vulnerability false positive rate in existing solutions, which limits practical applications. Software code vulnerabilities are usually caused by function key statements. Therefore, based on binary function decompilation, we identify the key statement that causes the vulnerability, analyze the cross-function data dependency of the key statements, extract the code similarity features and code vulnerability features of the key statements, and propose a feature filtering-based Binary vulnerability detection method SimFilter. SimFilter calculates the similarity of key statements by comparing the characteristics of function key statements, and infers the similarity of binary function vulnerabilities based on the similarity of key statements. The experimental results show that the vulnerability semantic features extracted by SimFilter are highly interpretable, and the accuracy rate is increased by 10.8% compared with other models. At the same time, we analyzed and evaluated the impact of different similarity features on the accuracy of SimFilter, and gave the best empirical value of the SimFilter model in firmware vulnerability detection. © 2023 IEEE.

关键词： Firmware

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BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks

BContext2Name: Naming Functions in Stripped Binaries with Mu...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Bing Xia Yunxiang Ge Ruinan Yang Jiabin Yin Jianmin Pang Chongjun Tang Key Laboratory of Mathematical Engineering and Advanced Computing dept.ZhongYuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis dept.ZhongYuan University of Technology Zhengzhou China dept.ZhongYuan University of Technology Zhengzhou China dept.Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

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Exploiting transmission lines on heterogeneous networks-on-chip to improve the adaptivity and efficiency of cache coherence 15

Exploiting transmission lines on heterogeneous networks-on-c...

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9th IEEE/ACM International Symposium on Networks-on-Chip, NOCS 2015

作者： Hu, Qi Liu, Peng Huang, Michael C. Xie, Xiang-Hui College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Dept. of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9781450333962

Emerging heterogeneous interconnects have shown lower latency and higher throughput, which can improve the efficiency of communication and create new opportunities for memory system designs. In this paper, transmission lines are employed as a latency-optimized network and combined with a packet-switched network to create heterogeneous interconnects improving the efficiencies of on-chip communication and cache coherence. We take advantage of this heterogeneous interconnect design, and keep cache coherence adaptively based on data locality. Different type of messages are adaptively directed through selected medium of the heterogeneous interconnects to enhance cache coherence effectiveness. Compared with a state-of-the-art coherence mechanism, the proposed technique can reduce the coherence overhead by 24%, reduce the network energy consumption by 35%, and improve the system performance by 25% on a 64-core system. Copyright is held by the owner/author(s).

关键词： Heterogeneous networks

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A Firmware Vulnerability Detection Method Based on Feature Filtering

A Firmware Vulnerability Detection Method Based on Feature F...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Bing Xia Chongjun Tang Wenbo Liu Shihao Chu Yu Dong dept Key Laboratory of Mathematical Engineering and Advanced Computing ZhongYuan University of Technology Zhengzhou China dept Henan Key Laboratory on Public Opinion Intelligent Analysis ZhongYuan University of Technology Zhengzhou China dept ZhongYuan University of Technology Zhengzhou China

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A Vulnerability Detection Method for Intermediate Code Based on a Relational Dependency Graph

A Vulnerability Detection Method for Intermediate Code Based...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Bing Xia Chongjun Tang Wenbo Liu Shihao Chu Yu Dong Dept. ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Dept. ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Dept. ZhongYuan University of Technology Zhengzhou China Dept. ZhongYuan University of Technology

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the advancement of information technology, the rapid increase in software systems has led to a growing demand for research on vulnerability detection. In recent years, the fast-paced development of deep learning has introduced new approaches and directions for this field. Existing source code vulnerability detection methods often fail to explicitly preserve the semantic information related to vulnerabilities in the source code, leading to difficulties in extracting vulnerable code features and high false-positive rates in detection. To address these issues, this study proposes a vulnerability detection method for intermediate code based on a relational dependency graph (RDG). After converting the source code into an intermediate representation, it is mapped to an RDG, which accurately expresses the data and control flow dependencies within the program. This approach retains critical node features and refines the types of control flow and data flow edges. By leveraging this method, it becomes possible to trace program data and control dependencies, understand data flow directions, and analyze interactions between different parts of the program, enabling the precise identification and interpretation of potential security vulnerabilities. Drawing on concepts from code property graphs (CPG), this method enhances the understanding and capture of complex relationships between nodes in graph-structured data. By performing vulnerability detection on intermediate code, this approach avoids false positives and negatives caused by language or platform-specific differences, thereby enabling deeper identification and interpretation of complex vulnerability patterns in source code. Experimental results on the SARD dataset demonstrate that the proposed method achieves a high precision rate of 98.7% for detecting command injection vulnerabilities (CWE78), significantly outperforming traditional methods.

关键词： Deep learning Privacy Codes Source coding Semantics Feature extraction Software systems Graph neural networks Security Information technology

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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