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In this work, we leverage the fact that MCS interface with their participants through mobile apps to design tools and new methodologies embodied in an end-to-end feedback-driven analysis framework which we use to study 10 popular and previously unexplored services in five different domains. However, to date, there is no comprehensive study on the extent of improper input validation (IIV) vulnerabilities and the feasibility of their exploits in MCSs across domains. Such attacks can be upsetting and even dangerous especially when they are used to inject improper inputs to mislead users. Prior work has shown that Foursquare and Waze (a location-based and a navigation MCS) are vulnerable to different kinds of data poisoning attacks. Mobile crowdsourcing services (MCS), enable fast and economical data acquisition at scale and find applications in a variety of domains. We mainly focus on NLP-based solutions under four categories: description-to-behaviour fidelity, description generation, privacy and malware detection. This study reviews these proposals and aim to explore possible research directions for future studies by presenting state-of-the-art in this domain. Especially, security solutions based on NLP have accelerated in the last 5 years and proven to be useful.
#ANDROID APPS SIMILAR TO TEXTLAB ANDROID#
The availability of such useful textual data together with the advancement in Natural Language Processing (NLP) that is used to process and understand textual data has encouraged researchers to investigate the use of NLP techniques in Android security. Therefore, beside application packages, such markets contain app information provided by app developers and app users. Unlike traditional application distribution mechanisms, Android applications are distributed centrally in mobile markets. In addition to the application code, Android applications have some metadata that could be useful for security analysis of applications. While attackers are improving their techniques, traditional solutions based on static and dynamic analysis have been also evolving.
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We discover inter-Įsting cases related to national ID, username/password,Īndroid is among the most targeted platform by attackers. SUPOR finds 355 apps with privacy disclosuresĪnd the false positive rate is 8.7%. SUPOR achieves an average precision of 97.3% and anĪverage recall of 97.3% for sensitive user input identifi-Ĭation. Ing SUPOR with off-the-shelf static taint analysis WeĪpply the system to 16,000 popular Android apps, andĬonduct a measurement study on the privacy disclosures. Privacy disclosures of sensitive user inputs by combin. The usefulness of SUPOR, we build a system that detects POR enables existing privacy analysis approaches to beĪpplied on sensitive user inputs as well. Such as user credentials, finance, and medical data. Tify sensitive user inputs containing critical user data, Ticular, we design and implement SUPOR, a novel staticĪnalysis tool that automatically examines the UIs to iden. In this paper, we examine the possibility of scalablyĭetecting sensitive user inputs from mobile apps. Of sensitive information, have been mostly neglected. Sensitive user inputs through UI (User Inter-įace), another information source that may contain a lot Ous mobile privacy related research efforts have largelyįocused on predefined known sources managed by smart. Tial part of our lives, privacy is a serious concern.
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While smartphones and mobile apps have been an essen.