| Android恶意应用HTTP行为特征生成与提取方法 |
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| 引用本文： | 罗亚玲,黎文伟,苏欣. Android恶意应用HTTP行为特征生成与提取方法[J]. 电信科学, 2016, 32(8): 136-145. DOI: 10.11959/j.issn.1000-0801.2016222 |
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| 作者姓名： | 罗亚玲 黎文伟 苏欣 |
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| 作者单位： | 1. 广东松山职业技术学院计算机系，广东 韶关 512126;2. 湖南大学信息科学与工程学院，湖南 长沙 410082;3. 湖南警察学院网络侦查技术湖南省重点实验室，湖南 长沙 410138 |
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| 基金项目： | 国家自然科学基金资助项目(61173168;61471169)，广东省教育厅资助项目(粤教高函[2012]54号-A12)，网络犯罪侦查湖南省普通高等学校重点实验室开放研究基金资助项目(No.2016WLFZZC008)The National Natural Science Foundation of China(61173168;61471169)，Foundation of the Education Department of Guangdong Province of China([2012]54-A12)，The Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges(2016WLFZZC008) |
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| 摘 要： | Android恶意应用数量的不断增加不仅严重危害Android市场安全，同时也为Android恶意应用检测工作带来挑战。设计了一种基于HTTP流量的Android恶意应用行为生成与特征自动提取方法。该方法首先使用自动方式执行恶意应用，采集所生成的网络流量。然后从所生成的网络流量中提取基于HTTP的行为特征。最后将得到的网络行为特征用于恶意应用检测。实验结果表明，所设计的方法可以有效地提取Android恶意应用行为特征，并可以准确地识别Android恶意应用。
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| 关 键 词： | Android恶意应用 HTTP流量 网络行为特征 安全 |
HTTP behavior characteristics generation and extraction approach for Android malware |
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| Yaling LUO,Wenwei LI,Xin SU. HTTP behavior characteristics generation and extraction approach for Android malware[J]. Telecommunications Science, 2016, 32(8): 136-145. DOI: 10.11959/j.issn.1000-0801.2016222 |
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| Authors: | Yaling LUO Wenwei LI Xin SU |
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| Affiliation: | 1. Department of Computer，Guangdong Songshan Polytechnic College，Shaoguan 512126，China;2. College of Computer Science and Electronics Engineering，Hunan University，Changsha 410082，China;3. Hunan Provincial Key Laboratory of Network Investigational Technology， Hunan Police Academy，Changsha 410138，China |
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| Abstract: | Growing of Android malware，not only seriously endangered the security of the Android market，but also brings challenges for detection.A generation and extraction approach of automatic Android malware behavioral signatures was proposed based on HTTP traffic.Firstly，the behavioral signatures were extracted from the traffic traces generated by Android malware.Then，network behavioral characteristics were extracted from the generated network traffic.Finally，these behavioral signatures were used to detect Android malware.The experimental results show that the approach is able to extract Android malware network traffic behavioral signature with accuracy and efficiency. |
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| Keywords: | Android malware HTTP traffic network behavioral characteristic security |
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