Android应用程序权限自动裁剪系统

Android系统使用权限机制对应用程序进行控制,即应用程序需要使用哪些系统资源就必须提前声明相应的权限。为了确保安全性和可靠性,应用程序声明权限时应该满足最小特权原则,即只声明其所需要使用到的最少权限,但现实中有很多应用存在权限过度声明的现象,给用户带来安全隐患。提出了一种Android应用程序权限自动裁剪系统PTailor,通过对Android应用程序安装文件（APK文件）进行分析和修改,使其满足最小特权原则。PTailor首先从APK文件中提取程序所调用的所有系统API,并在预先生成的API权限映射表中查找该API所对应的系统权限,从而得到应用程序实际使用到的最少权限列表。然后根据该权限列表对程序的权限声明文件进行修改,裁剪掉已声明但未使用的权限。最后将裁剪过的权限声明文件与程序的其他部分重新合并成新的APK文件,新的APK文件中除了所声明权限满足最小特权原则外,其结构和语义都没有发生改变。使用PTailor对现实中的1 246个Android应用进行权限裁剪实验,实验结果表明,PTailor能够在很短的时间内完成权限分析和裁剪,而且大多数被裁剪的程序都能够正确运行。     

一种扩展的Android应用权限管理模型

现有Android移动操作系统不支持用户自由分配已安装的应用权限。为解决该问题,提出一种细粒度的Android应用权限管理模型。该模型在保证系统安全性的前提下,对现有Android应用权限机制的框架层和应用层进行修改和扩展,使用户可以通过GUI界面按需分配系统中已安装的应用权限。实验结果表明,该模型能满足用户的Android应用权限管理需求,并且系统性能损失较小。     

基于权限的Android应用风险评估方法

针对Android权限机制存在的问题以及传统的应用风险等级评估方法的不足，提出了一种基于权限的Android应用风险评估方法。首先，通过对应用程序进行逆向工程分析，提取出应用程序声明的系统权限、静态分析的权限以及自定义的权限，和通过动态检测获取应用程序执行使用到的权限；然后，从具有恶意倾向的组合权限、"溢权"问题和自定义权限三个方面对应用程序进行量性风险评估；最后，采用层次分析法（AHP）计算上述三个方面的权重，评估应用的风险值。对6245个软件样本进行训练，构建自定义权限数据集和具有恶意倾向的权限组合数据集。实验结果表明，与Androguard相比，所提方法能更精确地评估应用软件的风险值。     

多层次的Android系统权限控制方法

随着Android智能平台的普及,其安全问题日益受到人们关注.在底层安全方面,部分root工具已经实现了对最新版本Android的root提权,从而给恶意软件滥用权限造成可乘之机;在上层应用安全方面,目前还没有能够在应用权限进行有效管理的方法.基于安全策略的思想,提出了一种Android应用权限动态管理机制,利用安全策略对授权进行描述,在Android框架层设置权限检查点,并调用请求评估算法进行授权评估,从而实现对应用行为的监控.实验结果表明,该方法能够有效管理Android应用权限的正常调用,约束非法调用,并且系统开销较小.     

Navigation and Exploration of Interconnected Pathways

Visualizing pathways, i. e. models of cellular functional networks, is a challenging task in computer assisted biomedicine. Pathways are represented as large collections of interwoven graphs, with complex structures present in both the individual graphs and their interconnections. This situation requires the development of novel visualization techniques to allow efficient visual exploration. We present the Caleydo framework, which incorporates a number of approaches to handle such pathways. Navigation in the network of pathways is facilitated by a hierarchical approach which dynamically selects a working set of individual pathways for closer inspection. These pathways are interactively rendered together with visual interconnections in a 2.5D view using graphics hardware acceleration. The layout of individual graphs is not computed automatically, but taken from the KEGG and BioCarta databases, which use layouts that life scientists are familiar with. Therefore they encode essential meta‐information. While the KEGG and BioCarta pathways use a pre‐defined layout, interactions such as linking+brushing, neighborhood search or detail on demand are still fully interactive in Caleydo. We have evaluated Caleydo with pathologists working on the determination of unknown gene functions. Informal experiences confirm that Caleydo is useful in both generating and validating such hypotheses. Even though the presented techniques are applied to medical pathways, the proposed way of interaction is not limited to cellular processes and therefore has the potential to open new possibilities in other fields of application.     

基于权限分析的 Android 应用程序检测系统

Android 系统在应用程序安装时仅给予粗略的权限提示界面,此界面不仅权限条目不全,而且解释异常粗略,普通用户完全看不懂,但基于使用需要,只能盲目确定授权。市面上的一些例如手机金山卫士,腾讯手机管家等管理软件,对于应用权限信息的查询要么权限条目远少于实际申请,要么权限解释一样粗略难懂,要么干脆就是直接调用 Android 系统 settings 下的粗略权限列表。〈br〉 通过研究 Android 的安全机制,在分析了上述现象可能导致的潜在安全隐患的基础上,文章设计开发了一种结合电脑端和手机端,能够对未安装的 APK 文件和已安装的 APP 应用程序进行深入权限检测系统。此系统可以检测出应用软件所申请的精确的权限个数和详细的权限列表,并通过建立数据库的方法给每条权限以及可能引起的安全问题辅以详尽、易懂的说明,使无专业知识的普通用户也可以弄懂所申请权限的作用,提高应用程序使用者的安全意识。此外,此系统还能提供用户针对某条敏感权限进行应用筛选,即列出手机内使用该敏感权限的所有应用,协助用户排查恶意软件,保护系统安全。〈br〉 针对 Android 平台开放性带来的用户隐私泄露和财产损失的问题,文章通过对 Android 安全机制的分析,给出了一种在电脑端和手机端的基于权限分析的 Android 应用程序检测系统。该系统能检测出各种应用的权限信息,也能检测出具有某条敏感权限的所有应用程序,为用户提供再判断的机会,可以更全面的保障用户信息和财产安全。     

Component-based permission management of Android applications

Most Android applications include third-party libraries (3PLs) to make revenues, to facilitate their development, and to track user behaviors. 3PLs generally require specific permissions to realize their functionalities. Current Android systems manage permissions in app (process) granularity. As a result, the permission sets of apps with 3PLs (3PL-apps) may be augmented, introducing overprivilege risks. In this paper, we firstly study how severe the problem is by analyzing the permission sets of 27 718 real-world Android apps with and without 3PLs downloaded in both 2016 and 2017. We find that the usage of 3PLs and the permissions required by 3PL-apps have increased over time. As a result, the possibility of overprivilege risks increases. We then propose Perman, a fine-grained permission management mechanism for Android. Perman isolates the permissions of the host app and those of the 3PLs through dynamic code instrumentation. It allows users to manage permission requests of different modules of 3PL-apps during app runtime. Unlike existing tools, Perman does not need to redesign Android apps and systems. Therefore, it can be applied to millions of existing apps and various Android devices. We conduct experiments to evaluate the effectiveness and efficiency of Perman. The experimental results verify that Perman is capable of managing permission requests of the host app and those of the 3PLs. We also confirm that the overhead introduced by Perman is comparable to that by existing commercial permission management tools.     

一种基于元信息的Android恶意软件检测方法

Android应用普遍具有比所属类型更多的功能,需要获取更多的权限,过多的权限可能带来一定的安全隐患。针对这类问题,提出一种基于元信息的Android恶意软件检测方法。首先,通过对Android应用程序描述进行LDA主题提取,实现数据降维,使用K-means聚类算法按照功能类型对应用程序分组;然后,对属于同一功能类型的所有应用程序提取其权限信息,以权限特征为研究对象,使用KNN算法进行Android恶意软件的分类检测。实验结果获得94.81%的平均准确率,证明了方法的有效性和高准确率。     

Pathfinder: Visual Analysis of Paths in Graphs

The analysis of paths in graphs is highly relevant in many domains. Typically, path‐related tasks are performed in node‐link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path‐related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder, a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node‐link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node‐link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.     

基于关联分析的Android权限滥用攻击检测系统

为了限制应用软件的行为,Android系统设计了权限机制.然而对于用户授予的权限,Android应用软件却可以不受权限机制的约束,任意使用这些权限,造成潜在的权限滥用攻击.为检测应用是否存在权限滥用行为,提出了一种基于关联分析的检测方法.该方法动态检测应用的敏感行为与用户的操作,并获得两者的关联程度.通过比较待检测应用与良性应用的关联程度的差别,得到检测结果.基于上述方法,设计并实现了一个原型系统DroidDect.实验结果表明,DroidDect可以有效检测出Android应用的权限滥用行为,并具有系统额外开销低等优点.     

基于权限分析的Android隐私数据泄露动态检测方法

针对现有Android平台隐私数据泄露动态检测方法检测效率较低的情况,文章设计并实现了一种基于权限分析的Android隐私数据泄露动态检测方法。该方法将Android静态检测中的权限分析与动态污点检测结合,根据应用程序申请的权限确定动态污点检测的隐私数据类型和隐私出口类型。检测选项保存在系统属性中。实验结果显示,该方法能够在保证隐私数据泄露检测有效性的前提下,提高动态污点检测的效率。     

基于Android重打包的应用程序安全策略加固系统设计

随着移动互联网快速发展,智能手机面临着严峻的安全威胁。由于Android权限划分的粗粒度以及权限授权一次性的约束,对于应用申请的权限,用户要么全部接受,要么拒绝安装,对于安装后应用可疑行为束手无策。为此文章设计了利用重打包注入安全防护代码的加固系统,不仅提供应用动态权限管理机制,还提供权限划分细粒度的安全策略,从源头上遏制恶意代码行为,用户也可根据需求定制策略,满足不同安全需求的安全加固应用。     

Cybersecurity for Android Applications: Permissions in Android 5 and 6

Android 5 informs users of all permissions requested when downloading an app and gives users an all-or-nothing acceptance decision to make for the permissions. In contrast, Android 6 informs users of each permission upon first use of the downloaded app. We conducted an online study with participants recruited through Amazon Mechanical Turk to compare the relative usability of the two permissions interfaces. Each interface condition contained a simulation of the Google Play Store and instructed participants to role-play the task of downloading an app. Afterward, each participant was questioned about which permissions were seen and the functions of those permissions. The Android 5 interface showed better performance with informing users as to which permissions access their device, whereas the Android 6 interface fared better with presenting the functions of the permissions. Also, the Android 6 interface was found to be more intuitive to use than that of Android 5. Although a pilot study showed that users favored the Android 6 permissions interface over Android 5’s, the present study found no clear evidence that it was more effective than Android 5.     

国内第三方Android应用市场安全性的检测

根据目前第三方Android应用市场应用存在的重新打包行为,随机选取国内官方的150个应用以及作为对比的第三方应用市场的572个同款应用,设计了Android重新打包应用安全检测系统。该系统先进行相似性计算,细粒度识别出重新打包应用,再通过逆向工程获得其资源文件,根据系统API与权限之间的映射匹配分析其越权行为,并根据构建的方法控制流图分析其权限滥用行为。系统通过并行化处理检测出第三方应用市场存在33.17%的重新打包应用,其中19.58%修改了权限。在修改过权限的应用中,45.95%存在越权行为,27.03%存在滥用权限行为。     

Android应用程序权限组重要性分析

Android系统运行时权限机制提出了权限组概念,但是应用市场上缺乏描述应用程序权限的信息,用户无法了解应用程序中权限组的重要性及其设置策略。针对这些问题,本文提出一种权限组重要性分析方法,通过逆向工程技术分析应用程序,利用排序算法计算程序中权限组的重要性评分,根据重要性评分确定应用程序中权限组的使用情况和设置策略,借助机器学习方法对分析方法进行测试与评估。实验结果表明,本文方法可以详细分析应用程序中权限组的重要程度,并推荐合适的权限组设置策略。     

基于权限相关性的Android恶意软件检测

针对Android平台恶意软件检测需求和Android权限特征冗余的问题,提出一套从权限相关性角度快速检测恶意软件的方案。采用卡方检验计算各权限属性对于分类结果的影响大小,去除冗余权限特征,再对权限属性聚类,提取代表性权限特征,进一步减少冗余。最后利用基于不同权限特征权重的改进朴素贝叶斯算法进行软件分类。在收集的2000个软件样本上进行了实验,恶意软件漏检率为10.33%,总体预测准确率达到88.98%。实验结果表明,该方案利用少量权限特征,能够初步检测Android应用软件是否有恶意倾向,为深入判断分析提供参考依据。     

TimeArcs: Visualizing Fluctuations in Dynamic Networks

In this paper we introduce TimeArcs, a novel visualization technique for representing dynamic relationships between entities in a network. Force‐directed layouts provide a way to highlight related entities by positioning them near to each other Entities are brought closer to each other (forming clusters) by forces applied on nodes and connections between nodes. In many application domains, relationships between entities are not temporally stable, which means that cluster structures and cluster memberships also may vary across time. Our approach merges multiple force‐directed layouts at different time points into a single comprehensive visualization that provides a big picture overview of the most significant clusters within a user‐defined period of time. TimeArcs also supports a range of interactive features, such as allowing users to drill‐down in order to see details about a particular cluster. To highlight the benefits of this technique, we demonstrate its application to various datasets, including the IMDB co‐star network, a dataset showing conflicting evidences within biomedical literature of protein interactions, and collocated popular phrases obtained from political blogs.     

基于Android权限机制的动态隐私保护模型

针对Android平台自身粗粒度权限机制的缺陷以及缺乏有效预防程序间隐私泄露机制的问题,提出一种改进的细粒度权限配置机制与隐私数据动态着色隔离相结合的Android隐私保护模型。通过对Android应用程序权限进行细粒度的动态配置,阻断隐私数据从程序内部泄露的途径,利用隐私数据着色跟踪实现对程序间传播的包含不同隐私权限标签的消息的隔离控制。通过大量实验的反复测试,该模型可以有效保护Android程序内部的隐私数据,及时发现程序间权限提升攻击进而实现隐私数据隔离,从而全方位实现Android隐私数据的保护,并为以后相关研究提供了新的方向。     

Android application behavioural analysis for data leakage

An android application requires specific permissions from the user to access the system resources and perform required functionalities. Recently, the android market has experienced exponential growth, which leads to malware applications. These applications are purposefully developed by hackers to access private data of the users and adversely affect the application usability. A suitable tool to detect malware is urgently needed, as malware may harm the user. As both malware and clean applications require similar types of permissions, so it becomes a very challenging task to differentiate between them. A novel algorithm is proposed to identify the malware‐based applications by probing the permission patterns. The proposed method uses the k‐means algorithm to quarantine the malware application by obtaining permission clusters. An efficiency of 90% (approx.) is attained for malicious behaviour, which validates this work. This work substantiates the use of application permissions for potential applications in android malware detection.     

Image‐Based Edge Bundles: Simplified Visualization of Large Graphs

We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual representation of a graph's structure. For this, we first compute a hierarchical edge clustering of a given graph layout which groups similar edges together. Next, we render clusters at a user‐selected level of detail using a new image‐based technique that combines distance‐based splatting and shape skeletonization. The overall result displays a given graph as a small set of overlapping shaded edge bundles. Luminance, saturation, hue, and shading encode edge density, edge types, and edge similarity. Finally, we add brushing and a new type of semantic lens to help navigation where local structures overlap. We illustrate the proposed method on several real‐world graph datasets.