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16th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS)

作者： Ji, Yuede Cui, Lei Huang, H. Howie George Washington Univ Graph Comp Lab Washington DC 20052 USA

ISBN: (纸本)9781450382878

binary code similarity detection, which answers whether two pieces of binary code are similar, has been used in a number of applications, such as vulnerability detection and automatic patching. Existing approaches face two hurdles in their efforts to achieve high accuracy and coverage: (1) the problem of source-binary code similarity detection, where the target code to be analyzed is in the binary format while the comparing code (with ground truth) is in source code format. Meanwhile, the source code is compiled to the comparing binary code with either a random or fixed configuration (e.g., architecture, compiler family, compiler version, and optimization level), which significantly increases the difficulty of code similarity detection;and (2) the existence of different degrees of code similarity. Less similar code is known to be more, if not equally, important in various applications such as binary vulnerability study. To address these challenges, we design BugGraph, which performs source-binary code similarity detection in two steps. First, BugGraph identifies the compilation provenance of the target binary and compiles the comparing source code to a binary with the same provenance. Second, BugGraph utilizes a new graph triplet-loss network on the attributed control flow graph to produce a similarity ranking. The experiments on four real-world datasets show that BugGraph achieves 90% and 75% true positive rate for syntax equivalent and similar code, respectively, an improvement of 16% and 24% over state-of-the-art methods. Moreover, BugGraph is able to identify 140 vulnerabilities in six commercial firmware.

关键词： code Similarity Vulnerability binary code Graph Embedding

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VESTIGE: Identifying binary code Provenance for Vulnerability Detection 19th

VESTIGE: Identifying Binary Code Provenance for Vulnerabilit...

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19th International Conference on Applied Cryptography and Network Security (ACNS)

作者： Ji, Yuede Cui, Lei Huang, H. Howie George Washington Univ Graph Comp Lab Washington DC 20037 USA

ISBN: (纸本)9783030783754

Identifying the compilation provenance of a binary code helps to pinpoint the specific compilation tools and configurations that were used to produce the executable. Unfortunately, existing techniques are not able to accurately differentiate among closely related executables, especially those generated with minor different compiling configurations. To address this problem, we have designed a new provenance identification system, VESTIGE. We build a new representation of the binary code, i.e., attributed function call graph (AFCG), that covers three types of features: idiom features at the instruction level, graphlet features at the function level, and function call graph at the binary level. VESTIGE applies a graph neural network model on the AFCG and generates representative embeddings for provenance identification. The experiment shows that VESTIGE achieves 96% accuracy on the publicly available datasets of more than 6,000 binaries, which is significantly better than previous works. When applied for binary code vulnerability detection, VESTIGE can help to improve the top-1 hit rate of three recent code vulnerability detection methods by up to 27%.

关键词： Compilation provenance code similarity Vulnerability binary code Graph neural network

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Numerical Analysis of All-Optical binary to Gray code Converter Using Silicon Microring Resonator

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OPTICAL MEMORY AND NEURAL NETWORKS 2024年第2期33卷 193-204页

作者： Hossain, Manjur Mondal, Kalimuddin Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai 602105 India

Present manuscript designs and analyzes numerically all-optical binary-to-gray code (BTGC) converter utilizing silicon microring resonator. A waveguide-based silicon microring resonator has been employed to achieve optical switching under low-power conditions using the two-photon absorption effect. Gray code (GC) is a binary numerical system in which two consecutive codes distinguished by only one bit. The GC is critical in optics communication because it prevents spurious output from optical switches and facilitates error correction in optical communications. MATLAB is used to design and analyze the architecture at almost 260 Gbps operational speed. The faster response times and compact design of the demonstrated circuits make them especially useful for optical communication systems. Performance indicating factors evaluated from MATLAB results and analyzed. Design parameters that are optimized have been chosen in order to construct the model practically.

关键词： microring resonator (MRR) all-optical switch binary code gray code code conversion

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Neural Representation Learning Based binary code Authorship Attribution 1

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11th International Conference on Digital Forensics and Cyber Crime

作者： Wang, Zhongmin Feng, Zhen Tian, Zhenzhou Xian Univ Posts & Telecommun Xian Peoples R China

ISBN: (数字)9783030687342

ISBN: (纸本)9783030687335;9783030687342

Authorship attribution on binary code is of great value in applications such as malware analysis, software forensics, and code theft detection. Inspired by the recent great successes of neural network and representation learning in various program analysis tasks, this study proposes NMPI to achieve fine-grained program authorship attribution by analyzing the binary codes of individual functions from the perspective of sequence and structural. To evaluate the NMPI, the study constructs a large dataset consisting of 268796 functions collected from Google codeJam. The extensive experimental evaluation shows that NMPI can achieve 91% accuracy for the function-level binary code authorship attribution task.

关键词： Authorship attribution binary code Neural network Representation learning

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All-optical graphene-based plasmonic binary to gray code converter

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OPTICAL AND QUANTUM ELECTRONICS 2022年第3期54卷 142-142页

作者： Abbasi, Mahmoud Sadeghi, Mojtaba Adelpour, Zahra Islamic Azad Univ Dept Elect Engn Shiraz Branch Shiraz Iran

In this paper, a binary to gray code converter based on graphene-plasmonic waveguides using the interference effect is proposed. The plasmonic waveguides are made of graphene nano-ribbons located on the dielectric layer. The use of graphene leads to high confinement of light, relatively low losses, and working in the mid-infrared to THz region. Based on the proposed structure, two-bit and three-bit binary to gray code converters are designed and their performance is evaluated using the finite-difference time-domain (FDTD) method. The calculated values of extinction ratio (ER) for two-bit converter are 7.22 dB and 14.83 dB for G0 and G1 bits and 8.82 dB, 5.72 dB, and 11.27 dB for G0, G1, and G2 bits in the three-bit converter. Time responses indicate the high performance of the proposed converters. Moreover, the dimensions of the two-bit and three-bit structures are 2.6 mu m(2) and 4 mu m(2), respectively, representing the high potential of these devices for use in integrated photonic systems. It is worthy to note that the converters with more bits can also be implemented using the proposed structure.

关键词： binary code Gray code Interference effect Optical converter Plasmonic waveguide

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Functionality Recognition on binary code with Neural Representation Learning 21

Functionality Recognition on Binary Code with Neural Represe...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Zhenzhou Tian Jinrui Li Peng Xue Jie Tian Hengchao Mao Yaqian Huang Xi'an University of Posts and Telecommunications School of Computer Science and Technology China

ISBN: (纸本)9781450384087

The functionality recognition of binary code has important application value in malware analysis, software forensics, binary code similarity analysis and other applications. Most of the existing methods are based on source code or machine learning strategies to carry out program similarity analysis, and this similarity analysis is also applied to a pair of programs, there are limitations in detection accuracy and quantity. Inspired by the recent great success of neural networks and representation learning in various program analysis tasks, We propose NPFI to analyze the binary code of the program and identify its functionality from the perspective of assembly instruction sequence. To evaluate the performance of NPFI, we built a large dataset consisting of 39,000 programs from six different categories collected from Google code Jam. A large number of experiments show that the accuracy of NPFI in binary code function recognition can reach 95.8%, which is much better than the existing methods.

关键词： binary code Neural network Program functionality identification

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BinDeep: A deep learning approach to binary code similarity detection

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114348-114348页

作者： Tian, Donghai Jia, Xiaoqi Ma, Rui Liu, Shuke Liu, Wenjing Hu, Changzhen Beijing Inst Technol Beijing Key Lab Software Secur Engn Tech Beijing 100081 Peoples R China North Univ China Shanxi Mil & Civilian Integrat Software Engn Tech Taiyuan 030051 Peoples R China Chinese Acad Sci Inst Informat Engn Key Lab Network Assessment Technol Beijing 100093 Peoples R China

binary code similarity detection (BCSD) plays an important role in malware analysis and vulnerability discovery. Existing methods mainly rely on the expert's knowledge for the BCSD, which may not be reliable in some cases. More importantly, the detection accuracy (or performance) of these methods are not so satisfied. To address these issues, we propose BinDeep, a deep learning approach for binary code similarity detection. This method firstly extracts the instruction sequence from the binary function and then uses the instruction embedding model to vectorize the instruction features. Next, BinDeep applies a Recurrent Neural Network (RNN) deep learning model to identify the specific types of two functions for later comparison. According to the type information, BinDeep selects the corresponding deep learning model for similarity comparison. Specifically, BinDeep uses the Siamese neural networks, which combine the LSTM and CNN to measure the similarities of two target functions. Different from the traditional deep learning model, our hybrid model takes advantage of the CNN spatial structure learning and the LSTM sequence learning. The evaluation shows that our approach can achieve good BCSD between cross-architecture, cross-compiler, cross-optimization, and cross-version binary code.

关键词： binary code Deep learning Similarity comparison Siamese neural network LSTM CNN

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Deep Learning Triplet Ordinal Relation Preserving binary code for Remote Sensing Image Retrieval Task

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REMOTE SENSING 2021年第23期13卷 4786-4786页

作者： Wang, Zhen Wu, Nannan Yang, Xiaohan Yan, Bingqi Liu, Pingping Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Sch Comp Sci & Technol Changchun 130012 Peoples R China

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

关键词： remote sensing image retrieval hashing algorithm binary code triplet ordinal relation preserving cross entropy

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Cross-Language binary-Source code Matching with Intermediate Representations 29

Cross-Language Binary-Source Code Matching with Intermediate...

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29th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Gui, Yi Wan, Yao Zhang, Hongyu Huang, Huifang Sui, Yulei Xu, Guandong Shao, Zhiyuan Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & TechnolCluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan Peoples R China Univ Newcastle Newcastle NSW Australia Huazhong Univ Sci & Technol Sch Math & Stat Wuhan Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781665437868

binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks.

关键词： Cross-language clone detection intermediate representation binary code code matching deep learning

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A new algorithm of links labelling for the isomorphism detection of various kinematic chains using binary code

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MECHANISM AND MACHINE THEORY 2019年 131卷 1-32页

作者： Rai, Rajneesh Kumar Punjabi, Sunil Ujjain Engn Coll Dept Mech Engn Ujjain 456010 Madhya Pradesh India

Isomorphism of kinematic chains (KCs) has always been a critical issue for the researchers dealing with structural synthesis. Consequently, many researchers of repute presented various methods during the last eight decades for the KCs with either simple and/or multiple joints. binary code is one of such various methods, but the major problem lies in the algorithm of links labelling, which becomes cumbersome, in particular, for large KCs. The paper presents a simple algorithm of links labelling used to find out a binary sequence which, in turn, provides a maximum binary code (chain invariant). The algorithm is tested for six, seven, eight, nine, ten, eleven, twelve and fifteen links with simple joints, seven and eight links KCs with multiple joints and finally, the Epicyclic gear trains (EGTs) with four, five and six links for its efficiency and reliability. The results are in full agreement with the references taken for the purpose. The paper discusses, in a unique way, the decoding of the binary codes of different KCs also. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Algorithm binary sequence binary code Isomorphism detection Links labelling Structural synthesis

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