共查询到19条相似文献,搜索用时 62 毫秒
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陈弘晟 《信息技术与信息化》2021,(7):96-98
为更好地了解基于深度学习的Android恶意应用检测领域的研究现状,对该领域现有的研究工作进行了综述.首先介绍了 Android恶意应用检测技术的发展以及主要方法,然后阐述了四种主流深度神经网络的基本原理,并从网络结构、特征工程和应用效果等方面对深度神经网络在Android恶意应用检测中的应用现状进行了总结,最后对基于... 相似文献
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基于权限频繁模式挖掘算法的Android恶意应用检测方法 总被引:1,自引:0,他引:1
Android应用所申请的各个权限可以有效反映出应用程序的行为模式,而一个恶意行为的产生需要多个权限的配合,所以通过挖掘权限之间的关联性可以有效检测未知的恶意应用。以往研究者大多关注单一权限的统计特性,很少研究权限之间关联性的统计特性。因此,为有效检测Android平台未知的恶意应用,提出了一种基于权限频繁模式挖掘算法的Android恶意应用检测方法,设计了能够挖掘权限之间关联性的权限频繁模式挖掘算法—PApriori。基于该算法对49个恶意应用家族进行权限频繁模式发现,得到极大频繁权限项集,从而构造出权限关系特征库来检测未知的恶意应用。最后,通过实验验证了该方法的有效性和正确性,实验结果表明所提出的方法与其他相关工作对比效果更优。 相似文献
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《信息技术》2016,(9):214-218
随着智能手机的快速普及,智能手机恶意APP的数量与日俱增。恶意行为代码的二次复用开发、恶意APP的自动生成技术使得具有恶意行为的APP开发效率大大提高,恶意程序的数量急剧上升,现有的恶意行为特征库分类繁杂、良莠不齐,不利于对恶意APP进行恶意行为分析。一个全面、稳定、可扩展的恶意行为特征库,能有效地提高对恶意行为软件的检测精度,有利于分析恶意行为的不断演化的特征。文中基于APP逆向工程研究提出了一个基于文本挖掘以及信息检索的恶意特征库构建方法,并通过构建恶意行为演化关系树对恶意行为簇之间的演化关系进行了分析,经过实验验证本文提出的构建恶意行为特征库方法对静态分析恶意应用提供了可靠的基础,提高了恶意行为检测精度。 相似文献
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通过介绍基于Linux内核的Android操作系统所面临的被恶意软件威胁的现状,引出静态分析。本文首先介绍了静态分析的对象、常用的静态分析方法及分类,然后从静态分析的一般流程和常见恶意行为的静态分析以及分析中存在的问题方面介绍了静态分析研究的现状,最后根据静态分析的现状和特点提出了进一步研究的方向。 相似文献
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由于Android系统应用市场的特性导致恶意软件传播迅速,对用户的手机乃至个人隐私造成了十分巨大的危害。本文首先介绍了Android应用的逆向技术,然后分析了恶意代码采用的多种Android代码隐藏技术及隐私获取的代码特征。针对这些情况,本文基于Android的逆向工程提出了一种静态检测和动态检测相结合的恶意行为检测方法,可以更加有效的检测代码中的恶意行为。最后通过对Android样本应用的分析表明此方法的可行性与有效性。 相似文献
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当前智能手机市场中,Android占有很大的市场份额,又因其他的开源,基于Android系统的智能手机很容易成为攻击者的首选目标。随着对Android恶意软件的快速增长,Android手机用户迫切需要保护自己手机安全的解决方案。为此,对多款Android恶意软件进行静态分析,得出Android恶意软件中存在危险API列表、危险系统调用列表和权限列表,并将这些列表合并,组成Android应用的混合特征集。应用混合特征集,结合主成分分析(PCA)和支持向量机(SVM),建立Android恶意软件的静态检测模型。利用此模型实现仿真实验,实验结果表明,该方法能够快速检测Android应用中恶意软件,且不用运行软件,检测准确率较高。 相似文献
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A new similarity detection scheme based on hierarchical SimHash algorithm was proposed.The scheme extractd contents from different aspects to represent the APK file,then used the improved SimHash to respectively represent the file.The scheme analyzed the APK file by extracting the AndroidManifest.xml file in it,the sum of the Smali code from the decompilation of dex file,instructions extracted in Smali files,Java code set,and instructions extracted in Java code files.Through the study of Voted Perceptron voting algorithm,the scheme used trust weight method,by valuating a trust weight in every layer,then combined all the result with weight in every layer as a resule of scheme,the result can be more reasonable and more convincing. 相似文献
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For the dramatic increase in the number and variety of mobile malware had created enormous challenge for information security of mobile network users,a value-derivative GRU-based mobile malware traffic detection approach was proposed in order to solve the problem that it was difficult for a RNN-based mobile malware traffic detection approach to capture the dynamic changes and critical information of abnormal network traffic.The low-order and high-order dynamic change information of the malicious network traffic could be described by the value-derivative GRU approach at the same time by introducing the concept of “accumulated state change”.In addition,a pooling layer could ensure that the algorithm can capture key information of malicious traffic.Finally,simulation were performed to verify the effect of accumulated state changes,hidden layers,and pooling layers on the performance of the value-derivative GRU algorithm.Experiments show that the mobile malware traffic detection approach based on value-derivative GRU has high detection accuracy. 相似文献
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Android智能终端安全综述 总被引:3,自引:0,他引:3
针对Android智能终端安全问题,构建Android智能终端安全分层体系。首先从远程防盗、生物身份验证和硬件安全模块方面阐述了Android设备安全的安全威胁及保护措施,然后从无线安全网络、病毒传播查杀和防钓鱼攻击说明了Android网络安全的隐患及防范,之后从内核安全、本地库运行时环境安全和应用框架安全角度介绍了Android操作系统安全的研究内容,接着从静态检测和应用行为动态检测、应用加固和应用评估方面展示了Android应用安全的研究成果,接下来着眼于数据本身总结了Android数据的追踪、加密和备份等安全保护技术,最后结合实际需求展望了Android安全未来在安全增强框架、智能应用行为分析等方向的发展。 相似文献
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The Android platform is the most popular mobile operating system. With the increase of the number of Android users, a lot of security issues have occurred. In order to detect the malicious behaviors for the installed Android Apps, in this paper, we propose an Android malware detecting scheme by integrating static and dynamic analysis methods. We use Androguard and DroidBox to extract the features, and then remove the irrelevant features. Then we employ the support vector machine (SVM) to classify the Android malware and benignware. From the result of our proposed scheme, the proposed integrated static and dynamic analysis scheme with SVM can effectively detect the Android malware. 相似文献
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《Digital Communications & Networks》2022,8(6):1040-1047
In recent years, we have witnessed a surge in mobile devices such as smartphones, tablets, smart watches, etc., most of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular, they are now the primary target of mobile malware, which could lead to both privacy leakage and property loss. To address the rapidly deteriorating security issues caused by mobile malware, various research efforts have been made to develop novel and effective detection mechanisms to identify and combat them. Nevertheless, in order to avoid being caught by these malware detection mechanisms, malware authors are inclined to initiate adversarial example attacks by tampering with mobile applications. In this paper, several types of adversarial example attacks are investigated and a feasible approach is proposed to fight against them. First, we look at adversarial example attacks on the Android system and prior solutions that have been proposed to address these attacks. Then, we specifically focus on the data poisoning attack and evasion attack models, which may mutate various application features, such as API calls, permissions and the class label, to produce adversarial examples. Then, we propose and design a malware detection approach that is resistant to adversarial examples. To observe and investigate how the malware detection system is influenced by the adversarial example attacks, we conduct experiments on some real Android application datasets which are composed of both malware and benign applications. Experimental results clearly indicate that the performance of Android malware detection is severely degraded when facing adversarial example attacks. 相似文献
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This paper proposes a network‐adaptive mechanism for HTTP‐based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time‐variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing. 相似文献