Identifying Software Theft Based on Classification of Multi-Attribute Features

Ye Guo; Mingyu Wang; Yangxia Luo

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Identifying Software Theft Based on Classification of Multi-Attribute Features

机译：基于多特征分类的软件盗窃识别

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Due to the low performance caused by the traditional "embedded" watermark and the shortages about low accuracy and weak anti-aggressive of single-attribute birthmark in checking obfuscated software theft, a software identification scheme is proposed which is based on classification of multi-dimensional features. After disassembly analysis and static analysis on protecting software and its resisting semantics-preserving transformations, the algorithm extracts features from many dimensions, which combines the statistic and semantic features to reflect the behavior characteristic of the software, analyzing and detecting theft based on similarities of software instead of traditional ways depending on a trusted third party or alone-similarity threshold. Through giving the formal description about the algorithm, depicting the algorithm realization, after comparisons and analysis from the qualitative and quantitative, theoretical and experimental aspects, the results show that the algorithm contributes to the resistance to attacks, as well as the robustness and credibility, and has advantages compared with similar methods.

机译：针对传统“嵌入”水印的性能低下，单属性胎记准确性低，抗攻击性弱等缺点，提出了一种基于多维分类的软件识别方案。特征。在对保护软件及其抗语义转换进行反汇编分析和静态分析后，该算法从多个维度提取特征，结合统计和语义特征来反映软件的行为特征，并基于软件的相似性对盗窃行为进行分析和检测。而不是依靠受信任的第三方或单独相似性阈值的传统方式。通过对算法进行形式化描述，描述算法的实现，从定性，定量，理论和实验等方面进行比较和分析，结果表明该算法有助于抵御攻击，增强鲁棒性和可信度，与同类方法相比具有优势。

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《Journal of Computers》 |2014年第6期|共11页
作者
Ye Guo; Mingyu Wang; Yangxia Luo;
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中图分类计算技术、计算机技术;
关键词
software theftclassification learningmulti-dimension featuressoftware birthmark;

机译：软件盗窃分类学习多维特征软件胎记;

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1. Identifying Software Theft Based on Classification of Multi-Attribute Features [J] . Ye Guo, Mingyu Wang, Yangxia Luo Journal of software . 2014,第6期

机译：基于多特征分类的软件盗窃识别
2. Identifying Software Theft Based on Classification of Multi-Attribute Features [J] . Ye Guo, Mingyu Wang, Yangxia Luo Journal of Computers . 2014,第6期

机译：基于多特征分类的软件盗窃识别
3. Identifying Software Theft Based on Classification of Multi-Attribute Features [J] . Ye Guo, Mingyu Wang, Yangxia Luo Journal of software . 2014,第6期

机译：基于多特征分类的软件盗窃识别
4. Feature Extraction based on Extended Multi-Attribute Profiles and Sparse Autoencoder for Remote Sensing Image Classification [C] . Hanane Teffahi, Hongxun Yao, Nasreddine Belabid, International Symposium on Multispectral Image Processing and Pattern Recognition . 2018

机译：基于扩展多属性配置文件和稀疏AutoEncoder进行遥感图像分类的功能提取
5. Data mining to identify optimal spatial aggregation scales and input features: Digital image classification with topographic LIDAR and LIDAR intensity returns. [D] . Tullis, Jason Alan. 2003

机译：数据挖掘以识别最佳的空间聚集尺度和输入特征：具有地形LIDAR和LIDAR强度返回的数字图像分类。
6. Combining multi-scale feature fusion with multi-attribute grading a CNN model for benign and malignant classification of pulmonary nodules [O] . Jumin Zhao, Chen Zhang, Dengao Li, 2020

机译：将多尺度特征融合与多属性分级相结合肺结核良性和恶性分类的CNN模型
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

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Identifying Software Theft Based on Classification of Multi-Attribute Features

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