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Open AccessProceedings Article

PiOS : Detecting privacy leaks in iOS applications

TLDR
To protect its users from malicious applications, Apple has introduced a vetting process, which should ensure that all applications conform to Apple’s (privacy) rules before they can be offered via the App Store, but this vetting process is not welldocumented.
Abstract
With the introduction of Apple’s iOS and Google’s Android operating systems, the sales of smartphones have exploded. These smartphones have become powerful devices that are basically miniature versions of personal computers. However, the growing popularity and sophistication of smartphones have also increased concerns about the privacy of users who operate these devices. These concerns have been exacerbated by the fact that it has become increasingly easy for users to install and execute third-party applications. To protect its users from malicious applications, Apple has introduced a vetting process. This vetting process should ensure that all applications conform to Apple’s (privacy) rules before they can be offered via the App Store. Unfortunately, this vetting process is not welldocumented, and there have been cases where malicious applications had to be removed from the App Store after

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Citations
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Proceedings ArticleDOI

Dissecting Android Malware: Characterization and Evolution

TL;DR: Systematize or characterize existing Android malware from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software.
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A study of android application security

TL;DR: A horizontal study of popular free Android applications uncovered pervasive use/misuse of personal/ phone identifiers, and deep penetration of advertising and analytics networks, but did not find evidence of malware or exploitable vulnerabilities in the studied applications.
Proceedings ArticleDOI

A survey of mobile malware in the wild

TL;DR: The incentives behind 46 pieces of iOS, Android, and Symbian malware that spread in the wild from 2009 to 2011 are analyzed and the effectiveness of techniques for preventing and identifying mobile malware is evaluated.
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Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

TL;DR: A permissionbased behavioral footprinting scheme to detect new samples of known Android malware families and a heuristics-based filtering scheme to identify certain inherent behaviors of unknown malicious families are proposed.
Proceedings ArticleDOI

PScout: analyzing the Android permission specification

TL;DR: An analysis of the permission system of the Android smartphone OS is performed and it is found that a trade-off exists between enabling least-privilege security with fine-grained permissions and maintaining stability of the permissions specification as the Android OS evolves.
References
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Journal ArticleDOI

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

TL;DR: TaintDroid as mentioned in this paper is an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data by leveraging Android's virtualized execution environment.

Program slicing

TL;DR: Applications of program slicing are surveyed, ranging from its first use as a debugging technique to current applications in property verification using finite state models, and a summary of research challenges for the slicing community is discussed.
Proceedings ArticleDOI

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

TL;DR: Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, this work found 68 instances of misappropriation of users' location and device identification information across 20 applications.
Proceedings ArticleDOI

Semantics-aware malware detection

TL;DR: Experimental evaluation demonstrates that the malware-detection algorithm can detect variants of malware with a relatively low run-time overhead and the semantics-aware malware detection algorithm is resilient to common obfuscations used by hackers.
Proceedings ArticleDOI

Pixy: a static analysis tool for detecting Web application vulnerabilities

TL;DR: This paper uses flow-sensitive, interprocedural and context-sensitive dataflow analysis to discover vulnerable points in a program and applies it to the detection of vulnerability types such as SQL injection, cross-site scripting, or command injection.
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