NativeProtector: Protecting Android Applications by Isolating and Intercepting Third-Party Native Libraries

Hong, Yu-Yang; Wang, Yu-Ping; Yin, Jie

doi:10.1007/978-3-319-33630-5_23

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…SVM is the best performer, as per the results [27]. e Native Protector system, which regulates third-party in Android apps, local libraries are used is proposed [28]. e server app and the client app are the two halves of the standalone Android app.…”

Section: Privacy-preserving Categorization Of Mobile Applicationsmentioning

confidence: 99%

Influencing User’s Behavior Concerning Android Privacy Policy: An Overview

Nazir

Deng

2021

Mobile Information Systems

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The wide-ranging implementation of Android applications used in various devices, from smartphones to intelligent television, has made it thought-provoking for developers. The permission granting mechanism is one of the defects imposed by the developers. Such assessing of defects does not allow the user to comprehend the implication of privacy for granting permission. Mobile applications are speedily easily reachable to typical users of mobile. Despite possible applications for improving the affordability, availability, and effectiveness of delivering various services, it handles sensitive data and information. Such data and information carry considerable security and privacy risks. Users are usually unaware of how the data can be managed and used. Reusable resources are available in the form of third-party libraries, which are broadly active in android apps. It provides a diversity of functions that deliver privacy and security concerns. Host applications and third-party libraries are run in the same process and share similar permissions. The current study has presented an overview of the existing approaches, methods, and tools used for influencing user behavior concerning android privacy policy. Various prominent libraries were searched, and their search results were analyzed briefly. The search results were presented in diverse perspectives for showing the details of the work done in the area. This will help researchers to offer new solutions in the area of the research.

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Section: Privacy-preserving Categorization Of Mobile Applicationsmentioning

confidence: 99%

Influencing User’s Behavior Concerning Android Privacy Policy: An Overview

Nazir

Deng

2021

Mobile Information Systems

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“…It uses Google's Native Client program in a separated thread to redirect specific calls for loading the modules which host the native code. NativeProtector [41] follows the processbased isolations of NativeGuard, and intercepts sensitive native calls to perform fine-grained access control.…”

Section: Related Workmentioning

confidence: 99%

$μ$Dep: Mutation-based Dependency Generation for Precise Taint Analysis on Android Native Code

Sun¹,

Ma²,

Zeng³

et al. 2021

Preprint

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The existence of native code in Android apps plays an essential role in triggering inconspicuous propagation of secrets and circumventing malware detection. However, the stateof-the-art information-flow analysis tools for Android apps all have limited capabilities of analyzing native code. Due to the complexity of binary-level static analysis, most static analyzers choose to build conservative models for a selected portion of native code. Though the recent inter-language analysis improves the capability of tracking information flow in native code, it is still far from attaining similar effectiveness of the state-ofthe-art information-flow analyzers that focus on non-native Java methods. To overcome the above constraints, we propose a new analysis framework, i.e., µDep, to detect sensitive information flows of the Android apps containing native code. In this framework, we combine a control-flow-based static binary analysis with a mutation-based dynamic analysis to model the tainting behaviors of native code in the apps. Based on the result of the analyses, µDep conducts a stub generation for the related native functions to facilitate the state-of-the-art analyzer, i.e., DroidSafe, with fine-grained tainting behavior summaries of native code. The experimental results show that our framework is competitive on the accuracy and effective in analyzing the information flows in real-world apps and malware compared with the state-of-the-art inter-language static analysis.

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“…A class of security solutions that has attracted researchers over the last twenty years consists of enabling software applications to become immune against attacks [1][2][3][4]. This is a challenging area as it integrates several domains including anomaly-based intrusion detection and prevention [5][6][7][8][9][10][11], application partitioning and sandboxing [12][13][14][15], automatic error detection and patching [3,4], as well as collaborative application communities [1,16,17]. To this end, not only can these fields be leveraged and combined in different ways, but they can also be approached from different perspectives and using different techniques.…”

Section: Introductionmentioning

confidence: 99%

Application-Level Unsupervised Outlier-Based Intrusion Detection and Prevention

Iraqi

Bakkali

2019

Security and Communication Networks

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As cyber threats are permanently jeopardizing individuals privacy and organizations’ security, there have been several efforts to empower software applications with built-in immunity. In this paper, we present our approach to immune applications through application-level, unsupervised, outlier-based intrusion detection and prevention. Our framework allows tracking application domain objects all along the processing lifecycle. It also leverages the application business context and learns from production data, without creating any training burden on the application owner. Moreover, as our framework uses runtime application instrumentation, it incurs no additional cost on the application provider. We build a fine-grained and rich-feature application behavioral model that gets down to the method level and its invocation context. We define features to be independent from the variable structure of method invocation parameters and returned values, while preserving security-relevant information. We implemented our framework in a Java environment and evaluated it on a widely-used, enterprise-grade, and open-source ERP. We tested several unsupervised outlier detection algorithms and distance functions. Our framework achieved the best results in terms of effectiveness using the Local Outlier Factor algorithm and the Clark distance, while the average instrumentation overhead per intercepted call remains acceptable.

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NativeProtector: Protecting Android Applications by Isolating and Intercepting Third-Party Native Libraries

Cited by 6 publications

References 15 publications

Influencing User’s Behavior Concerning Android Privacy Policy: An Overview

Influencing User’s Behavior Concerning Android Privacy Policy: An Overview

$μ$Dep: Mutation-based Dependency Generation for Precise Taint Analysis on Android Native Code

Application-Level Unsupervised Outlier-Based Intrusion Detection and Prevention

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