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

doi:10.1007/978-3-319-59439-2_3

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

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications
Editors	Iván Palomares Carrascosa, Harsha Kumara Kalutarage, Yan Huang
Place of Publication	Cham
Publisher	Springer International Publishing
Pages	55-97
Number of pages	43
ISBN (Print)	978-3-319-59439-2
DOIs	https://doi.org/10.1007/978-3-319-59439-2_3
Publication status	Published - 02 Aug 2017

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10.1007/978-3-319-59439-2_3

https://doi.org/10.1007/978-3-319-59439-2_3

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Mariuca, A. I., Blasco, J., Chen, T. M., Kalutarage, H. K., Muttik, I., Nguyen, H. N., Roggenbach, M., & Shaikh, S. A. (2017). Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case. In I. Palomares Carrascosa, H. K. Kalutarage, & Y. Huang (Eds.), Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications (pp. 55-97). Springer International Publishing. https://doi.org/10.1007/978-3-319-59439-2_3

Mariuca, As Irina ; Blasco, Jorge ; Chen, Thomas M. et al. / Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case. Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. editor / Iván Palomares Carrascosa ; Harsha Kumara Kalutarage ; Yan Huang. Cham : Springer International Publishing, 2017. pp. 55-97

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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{\textregistered}'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.",

author = "Mariuca, {As Irina} and Jorge Blasco and Chen, {Thomas M.} and Kalutarage, {Harsha Kumara} and Igor Muttik and Nguyen, {Hoang Nga} and Markus Roggenbach and Shaikh, {Siraj Ahmed}",

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Mariuca, AI, Blasco, J, Chen, TM, Kalutarage, HK, Muttik, I, Nguyen, HN, Roggenbach, M & Shaikh, SA 2017, Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case. in I Palomares Carrascosa, HK Kalutarage & Y Huang (eds), Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. Springer International Publishing, Cham, pp. 55-97. https://doi.org/10.1007/978-3-319-59439-2_3

Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case. / Mariuca, As Irina; Blasco, Jorge; Chen, Thomas M. et al.
Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. ed. / Iván Palomares Carrascosa; Harsha Kumara Kalutarage; Yan Huang. Cham: Springer International Publishing, 2017. p. 55-97.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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Mariuca AI, Blasco J, Chen TM, Kalutarage HK, Muttik I, Nguyen HN et al. Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case. In Palomares Carrascosa I, Kalutarage HK, Huang Y, editors, Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. Cham: Springer International Publishing. 2017. p. 55-97 doi: 10.1007/978-3-319-59439-2_3

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

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