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SSRN 2024年

作者： Xia, Bing Ge, Yunxiang Yin, Jiabin Yang, Ruinan Pang, Jianmin Tang, Chongjun ZhongYuan University of Technology Henan Zhengzhou450007 China Henan Key Laboratory of Cyberspace Situation Awareness Henan Zhengzhou450007 China Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450007 China

Context:Predicting function names in stripped binaries is a critical challenge in reverse engineering due to their lack of explicit semantic information. Existing machine learning approaches often suffer from poor generalization, especially in dealing with long-tail distributions of function names. Objective:This study introduces BContext2Name, a novel machine-learning model designed to improve the prediction of function names in stripped binaries. The primary goals are to enhance accuracy, address the long-tail effect, and improve generalization capabilities for unknown binaries. Method:BContext2Name incorporates multi-label learning, allowing it to generate multiple related labels from function context information, thus providing richer insights into binary function names. The model also utilizes enhanced control flow graphs to simulate function execution behavior, aiding in the understanding of function semantics. We evaluated the model using 72,074 binary functions from the GNU repository, covering a wide range of applications. Result:Our evaluation shows that BContext2Name surpasses current state-of-the-art methods in predictive accuracy. Specifically, it achieves an F1 score that is 3.55% higher than the XFL method and 15.23% higher than the Nero method. The model also demonstrates better generalization capabilities. Ablation studies confirm the significant impact of multi-label learning on the model's performance enhancement. Conclusion:BContext2Name sets a new benchmark in function name prediction for stripped binaries. By integrating multi-label learning with enhanced control flow graph analysis, the model not only improves prediction accuracy but also addresses key challenges in binary analysis, such as the long-tail effect and generalization issues. This approach represents a significant advancement in machine learning applications for reverse engineering, offering deeper insights into binary code functionalities. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Anti-obfuscation Binary Code Clone Detection Based on Software Gene

Anti-obfuscation Binary Code Clone Detection Based on Softwa...

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2021国际计算机前沿大会

作者： Ke Tang Fudong Liu Zheng Shan Chunyan Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing

Information technology facilitates people's lives greatly,while it also brings many security issues,such as code plagiarism,software in-fringement,and malicious *** order to solve the problems,reverse engineering is applied to analyze abundant binary code manually,which costs a lot of ***,due to the maturity of different obfuscation techniques,the disassembly code generated from the same function differs greatly in the opcode and control flow graph through different obfuscation *** paper propose a method inspired by natural language processing,to realize the semantic similarity matching of binary code in basic block granularity and function *** the similarity matching task of binary code obtained by different obfuscation options of LLVM,the indicator reaches 99%,which is better than the existing technologies.

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval 24

Focus, Distinguish, and Prompt: Unleashing CLIP for Efficien...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zeng, Gangyan Zhang, Yuan Wei, Jin Yang, Dongbao Zhang, Peng Gao, Yiwen Qin, Xugong Zhou, Yu School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Lenovo Research Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China TMCC College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798400706868

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP's particular advantages in handling diverse forms of query text. The source code will be available at https://***/Gyann-z/FDP. © 2024 ACM.

关键词： Semantics

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High-Order Local Discontinuous Galerkin Method with Multi-Resolution WENO Limiter for Navier-Stokes Equations on Triangular Meshes

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Communications in Computational Physics 2023年第5期33卷 1217-1239页

作者： Yizhou Lu Jun Zhu Shengzhu Cui Zhenming Wang Linlin Tian Ning Zhao State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China Key Laboratory of Mathematical Modeling and High Performance Computing of Air Vehicles Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China

In this paper,a new multi-resolution weighted essentially non-oscillatory(MR-WENO)limiter for high-order local discontinuous Galerkin(LDG)method is designed for solving Navier-Stokes equations on triangular *** MR-WENO limiter is a new extension of the finite volume MR-WENO *** new limiter uses information of the LDG solution essentially only within the troubled cell itself,to build a sequence of hierarchical L^(2)projection polynomials from zeroth degree to the highest degree of the *** an example,a third-order LDGmethod with associated same orderMR-WENO limiter has been developed in this paper,which could maintain the original order of accuracy in smooth regions and could simultaneously suppress spurious oscillations near strong shocks or contact *** linear weights of such new MR-WENO limiter can be any positive numbers on condition that their summation is *** is the first time that a series of different degree polynomials within the troubled cell are applied in a WENO-type fashion to modify the freedom of degrees of the LDG solutions in the troubled *** MR-WENO limiter is very simple to construct,and can be easily implemented to arbitrary high-order accuracy and in higher dimensions on unstructured *** spatial reconstruction methodology improves the robustness in the numerical simulation on the same compact spatial stencil of the original LDG methods on triangular *** classical viscous examples are given to show the good performance of this third-order LDG method with associated MR-WENO limiter.

关键词： Local discontinuous Galerkin method multi-resolution WENO limiter triangular meshes Navier-Stokes equations

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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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

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.

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Higher-Order Quantum Genetic Algorithm and Its Application on Non-Linear Equations 6

Higher-Order Quantum Genetic Algorithm and Its Application o...

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2021 6th International Conference on Electronic Technology and Information Science, ICETIS 2021

作者： Jin, Lei Shan, Zheng Hu, Chunchao Xu, Le School of Information Engineering Zhengzhou University Zhengzhou Henan450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan450000 China

The high efficiency of quantum algorithms is caused by the quantum parallelism of the superposition principle and the quantum entangled, but traditional quantum genetic algorithms only use the quantum superposition principle. In order to further improve the performance of the algorithm, this paper proposes a new higher-order quantum genetic algorithm, which adds quantum entanglement properties on the basis of the principle of quantum superposition. Finally, the traditional quantum genetic algorithm and the higher-order quantum genetic algorithm are used to test a set of nonlinear equations many times. The results show that compared with the traditional quantum genetic algorithm, the evolutionary update operation of the higher-order quantum genetic algorithm does not involve multiple judgment conditions of the traditional quantum gate, requires less evolutionary algebra, and can converge quickly. © Published under licence by IOP Publishing Ltd.

关键词： Nonlinear equations

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Research on Quantum Programming Framework Based on NISQ Function 6

Research on Quantum Programming Framework Based on NISQ Func...

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2021 6th International Conference on Electronic Technology and Information Science, ICETIS 2021

作者： Liu, Xiaonan Xu, Le Hu, Chunchao Jin, Lei School of Information Engineering Zhengzhou University Zhengzhou Henan450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan450000 China

In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints. © Published under licence by IOP Publishing Ltd.

关键词： Quantum computers

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Design and Implementation of Links Generation for Inter Domain Routing System 12

Design and Implementation of Links Generation for Inter Doma...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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