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Text Analytics for Mobile App Security and Beyond

Speakers: Tao Xie
Topic(s): Artificial Language/Machine Learning,Documentation,Knowledge Discovery in Data,Mobile Computing,Security & Information Protection,Software Engineering,Web Topics

 


Abstract
Mobile apps are accompanied by a rich amount of natural language text: app descriptions, app user reviews, update/release notes, etc. Such natural language text is essential in conveying important information about the apps (such as expected functionalities) and such information is not easily attainable from other structured information of the apps (such as app source or binary code, execution traces). Given the overwhelming amount of available natural language text, there is a high demand of text analytics including natural language processing (NLP) and text mining techniques to automatically analyze the natural language text to improve mobile app security. The history of applying NLP and text mining techniques to analyze software artifacts can date back to about a decade ago. Only till recently, text analytics for software artifacts such as mobile app artifacts has become an emerging research area in the security community. This talk presents recent work on automated analysis of natural language text for improving mobile app security, and software security in general. 

 


About this Lecture

Number of Slides: 50
Duration: 60 minutes
Languages Available: English
Last Updated: 06-10-2014
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Lancaster University

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