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Learning binary code Features for UAV Target Tracking 3

Learning Binary Code Features for UAV Target Tracking

引用

2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Qiao Xiao Qinyu Zhang Xi Wu Xiao Han Ronghua Li Harbin Institute of Technology Shenzhen

ISBN: (纸本)9781538604854

During target tracking, in order to obtain a higher tracking accuracy, the region we would like to track should have a good feature expression. Furthermore, we need to extract multilevel and complex features to deal with problems which are usually encountered during UAV tracking, such as the target deformation, scale change and occlusion. However, such features make tracker more complex which would seriously affect the real-time tracking. Considering the above problems, we take the advantage of random forest for features selection, and then transform the features to binary code, which can not only reduce redundancy but speed up the tracker. In order to further improve the accuracy of UAV tracking, we utilize structured SVM for online learning to distinguish object from background. In addition, we apply the scale pyramid to achieve the scale invariance of tracker, which help to obtain a more precise position of the object. We have verified the effectiveness and robustness of our method on the classical UAV object tracking dataset UAV123.

关键词： UAV tracking binary code random forest scale invariance structured SVM

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Neural Representation Learning Based binary code Authorship Attribution 1

引用

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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Scalable Image Search with Reliable binary code

Scalable Image Search with Reliable Binary Code

引用

ACM Conference on Multimedia (MM)

作者： Ren, Guangxin Cai, Junjie Li, Shipeng Yu, Nenghai Tian, Qi Univ Sci & Technol China Hefei Anhui Peoples R China Univ Texas San Antonio San Antonio TX USA Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781450330633

In many existing image retrieval algorithms, Bag-of-Words (BoW) model has been widely adopted for image representation. To achieve accurate indexing and efficient retrieval, local features such as the SIFT descriptor are extracted and quantized to visual words. One of the most popular quantization scheme is scalar quantization, which generates binary signature with an empirical threshold value. However, such binarization strategy inevitably suffers from the quantization loss induced by each quantized bit and impairs the effectiveness of search performance. In this paper, we investigate the reliability of each bit in scalar quantization and propose a novel reliable binary SIFT feature. We move one step ahead to incorporate the reliability in both index word expansion and feature similarity. Our proposed approach not only accelerates the search speed by narrowing search space, but also improves the retrieval accuracy by alleviating the impact of unreliable quantized bits. Experimental results demonstrate that the proposed approach achieves significant improvement in retrieval efficiency and accuracy.

关键词： binary code Image Search

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CBSDI: Cross-Architecture binary code Similarity Detection based on Index Table 22

CBSDI: Cross-Architecture Binary Code Similarity Detection b...

引用

22nd IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Deng, Longmin Zhao, Dongdong Zhou, Junwei Xia, Zhe Xiang, Jianwen Wuhan Univ Technol Sch Comp Sci & Artif Intelligence Hubei Key Lab Transportat Internet Things Wuhan Hubei Peoples R China Wuhan Univ Technol Chongqing Res Inst Chongqing Peoples R China

ISBN: (纸本)9781665477048

binary code similarity detection for cross-platform is widely used in plagiarism detection, malware detection and vulnerability search, aiming to detect whether two binary functions over different platforms are similar. Existing cross-architecture approaches mainly rely on the approximate matching calculation of complex high-dimensional features, such as graph, which are inevitably slow and unsuitable for large-scale applications. To solve this problem, we propose a novel approach based on index table called CBSDI, improving efficiency by screening a batch of mismatched functions before similarity detection. We select three features and compare them across architectures to select the most appropriate one to construct the index table, and this table can be embedded in other tools. The evaluation shows that the index table can roughly cut the computational costs in half when there are few errors. Moreover, compared with the related works in the literature, our proposed approach can improve not only the efficiency but also the accuracy.

关键词： binary code similarity detection cross-architecture

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LoongChecker: Practical summary-based semi-simulation to detect vulnerability in binary code

LoongChecker: Practical summary-based semi-simulation to det...

引用

Int Joint Conference of 10th IEEE Int Conf on Trust, Security and Privacy in Computing and Communications (TrustCom) / 8th IEEE Int Conf on Embedded Software and Systems (ICESS) / 6th Int Conf on Frontier of Computer Science and Technology (FCST)

作者： Cheng, Shaoyin Yang, Jun Wang, Jiajie Wang, Jinding Jiang, Fan Univ Sci & Technol China Informat Technol Secur Evaluat Ctr Hefei 230027 Anhui Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China

ISBN: (纸本)9780769546001

The automatic detection of security vulnerabilities in binary code is challenging and lacks efficient tools. This paper presents a novel semi-simulation approach to statically detect potential vulnerabilities in binary code. The semi-simulation approach simulates address related instructions accurately using value set analysis, and only traces data dependence on other instructions using data dependence analysis. We have implemented this approach on a tool called LoongChecker, and evaluate it on three real world programs, and detect three known vulnerabilities and two zero-day vulnerabilities. The results show our approach is practical and can be applied to large real world software.

关键词： Semi-simulation static analysis binary code vulnerability detection taint analysis function summary

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BHMVD: binary code-based Hybrid Neural Network for Multiclass Vulnerability Detection 20

BHMVD: Binary Code-based Hybrid Neural Network for Multiclas...

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20th IEEE Int Symposium on Parallel and Distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Cui, Ningning Chen, Liwei Du, Gewangzi Wu, Tongshuai Zhu, Chenguang Shi, Gang Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781665464970

Precise binary code vulnerability detection is a significant research topic in software security. Currently, the majority of software is released in binary form, and the corresponding vulnerability detection approaches for binary code are desired. Existing deep learning-based detection techniques can only detect binary code vulnerabilities but cannot precisely identify the types of vulnerabilities. This paper proposes a binary code-based Hybrid neural network for Multiclass Vulnerability Detection, dubbed BHMVD. BHMVD generates binary slices according to the control dependence and data dependence of library/API function calls, and then extracts syntax features from binary slices to generate type slices, which can help identify vulnerability types. This paper uses a hybrid neural network of CNN-BLSTM to extract vulnerability features from binary and type slices. The former extracts local features, while the latter extracts global features. Experiment results on 19 types of vulnerabilities show that BHMVD is effective for binary code-based multiclass vulnerability detection, and using a hybrid neural network can improve detection ability.

关键词： binary code Multiclass Vulnerability Detection Hybrid Neural Network Type Slices binary Slices

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Combined Approach to Solving Problems in binary code Analysis

Combined Approach to Solving Problems in Binary Code Analysi...

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9th International Conference on Computer Science and Information Technologies (CSIT)

作者： Getman, Alexander Padaryan, Vartan Solovyev, Mikhail Russian Acad Sci Inst Syst Programming Moscow 117901 Russia

This paper proposes a decomposition of generic software security problems, mapping them to smaller problems of static and dynamic binary code analysis.

ISBN: (纸本)9781479924608

This paper proposes a decomposition of generic software security problems, mapping them to smaller problems of static and dynamic binary code analysis.

关键词： Software security binary code dynamic and static analysis

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A New String Distance For Computing binary code Similarities 10

A New String Distance For Computing Binary Code Similarities

引用

IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Oprisa, Ciprian Cabau, George Pal, Gheorghe Sebestyen Bitdefender Bucharest Romania Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9781479965694

A binary program can be represented as a string of operation codes by disassembling it and removing the instruction operands. Using this representation, the problem of detecting if two programs are similar can be reduced to computing the distance between two strings. The most common types of edit distance are fit for this task but unfortunately their running time is quadratic. A new type of edit distance called deq distance is proposed and it can be computed in linear time. This distance also measures the number of operations required to transform a string into another but only allows the operations that can be performed on a double ended queue. The new distance has been tested in clustering algorithms and produced quality clusters, very similar to the ones produced by a regular edit distance. The performance evaluation also showed that the new distance can be computed faster for strings longer than 300 characters, making it appropriate for finding similarities between malware samples.

关键词： Benchmark testing binary codes Clustering algorithms Equations Malware Measurement Transforms binary code Clustering algorithms Spyware Benchmark testing Cosmic strings Transform

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Neural Network-based Graph Embedding for Cross-Platform binary code Similarity Detection 17

Neural Network-based Graph Embedding for Cross-Platform Bina...

引用

24th ACM-SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Xu, Xiaojun Liu, Chang Feng, Qian Yin, Heng Song, Le Song, Dawn Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA Samsung Res Amer Mountain View CA USA Univ Calif Riverside Riverside CA 92521 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450349468

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

关键词： binary code Similarity Detection Neural Network

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A Novel binary code Based Projector-Camera System Registration Method

A Novel Binary Code Based Projector-Camera System Registrati...

引用

International Conference on Computer Engineering and Technology

作者： Duan, Jiang Tumblin, Jack Southwestern Univ Finance & Econ Chengdu Peoples R China Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9780769535210

Precisely relating pixels in the projector image to pixels in camera image plays a significant role in many projector-camera systems. In this paper, we present a novel method of finding the correspondence in a simple and efficient manner. Our registration approach uses binary coding scheme, only need to capture a few pictures and does not need to give any assumption in the projector-camera system. Our experimental results demonstrate the advantage of using our registration methods.

关键词： binary code Projector-Camera System Registration binary code Registration IMAGE PROJECTORS pixel

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