{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:14:05Z","timestamp":1781104445689,"version":"3.54.1"},"reference-count":39,"publisher":"IGI Global Scientific Publishing","issue":"3","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<p>Mobile botnets are gaining popularity with the expressive demand of smartphone technologies. Similarly, the majority of mobile botnets are built on a popular open source OS, e.g., Android. A mobile botnet is a network of interconnected smartphone devices intended to expand malicious activities, for example; spam generation, remote access, information theft, etc., on a wide scale. To avoid this growing hazard, various approaches are proposed to detect, highlight and mark mobile malware applications using either static or dynamic analysis. However, few approaches in the literature are discussing mobile botnet in particular. In this article, the authors have proposed a hybrid analysis framework combining static and dynamic analysis as a proof of concept, to highlight and confirm botnet phenomena in Android-based mobile applications. The validation results affirm that machine learning approaches can classify the hybrid analysis model with high accuracy rate (98%) than classifying static or dynamic individually.<\/p>","DOI":"10.4018\/joeuc.2020070105","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T10:26:36Z","timestamp":1590661596000},"page":"50-67","source":"Crossref","is-referenced-by-count":9,"title":["Android Botnets"],"prefix":"10.4018","volume":"32","author":[{"given":"Ahmad","family":"Karim","sequence":"first","affiliation":[{"name":"Bahauddin Zakariya University, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8012-5852","authenticated-orcid":true,"given":"Victor","family":"Chang","sequence":"additional","affiliation":[{"name":"Aston University, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Firdaus","sequence":"additional","affiliation":[{"name":"Faculty of Computer Systems and Software Engineering, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JOEUC.2020070105-0","unstructured":"Android, S. 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Droidbox: An android application sandbox for dynamic analysis."},{"key":"JOEUC.2020070105-9","doi-asserted-by":"crossref","unstructured":"Fereidooni, H., Conti, M., Yao, D., & Sperduti, A. (2016). ANASTASIA: ANdroid mAlware detection using STatic analySIs of Applications. Paper presented at the 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS). Academic Press.","DOI":"10.1109\/NTMS.2016.7792435"},{"key":"JOEUC.2020070105-10","unstructured":"Gu, G., Zhang, J., & Lee, W. (2008). BotSniffer: Detecting botnet command and control channels in network traffic."},{"key":"JOEUC.2020070105-11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25645-0_6"},{"key":"JOEUC.2020070105-12","unstructured":"Kang, H. J., Jang, J. W., Mohaisen, A., & Kim, H. K. (2014). Androtracker: Creator information based android malware classification system. Paper presented at the Information Security Applications-15th International Workshop, WISA. 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