MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version)
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 847836
- Type
- D - Journal article
- DOI
-
10.1145/3313391
- Title of journal
- ACM Transactions on Privacy and Security
- Article number
- 14
- First page
- -
- Volume
- 22
- Issue
- 2
- ISSN
- 2471-2566
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
http://dx.doi.org/10.1145/3313391
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 24
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We built an Android malware detection system that models the sequences of API calls as Markov chains. Our results demonstrate that statistical behavioural models such as abstraction and Markov chain modelling of API call sequences are more robust that traditional techniques, therefore our work can form a basis of more advanced detection systems in the future. Our system won the second place at the Cyber Security Awareness Week Europe 2017 (Best Applied Research Award)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
