Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case

As Irina Mariuca, Jorge Blasco, Thomas M. Chen, Harsha Kumara Kalutarage, Igor Muttik, Hoang Nga Nguyen, Markus Roggenbach, Siraj Ahmed Shaikh

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Malware has been a major problem in desktop computing for decades. With the recent trend towards mobile computing, malware is moving rapidly to smartphone platforms. ``Total mobile malware has grown 151% over the past year'', according to McAfee®'s quarterly treat report in September 2016. By design, AndroidTM is ``open'' to download apps from different sources. Its security depends on restricting apps by combining digital signatures, sandboxing, and permissions. Unfortunately, these restrictions can be bypassed, without the user noticing, by colluding apps for which combined permissions allow them to carry out attacks. In this chapter we report on recent and ongoing research results from our ACID project which suggest a number of reliable means to detect collusion, tackling the aforementioned problems. We present our conceptual work on the topic of collusion and discuss a number of automated tools arising from it.
Original languageEnglish
Title of host publicationData Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications
EditorsIván Palomares Carrascosa, Harsha Kumara Kalutarage, Yan Huang
Place of PublicationCham
PublisherSpringer International Publishing
Pages55-97
Number of pages43
ISBN (Print)978-3-319-59439-2
DOIs
Publication statusPublished - 02 Aug 2017

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