iOS mobile malware analysis: a state-of-the-art

In earlier years, most malware attacks were against Android smartphones. Unfortunately, for the past few years, the trend has shifted towards attacks against the Apple iOS smartphone. Consequently, an in-depth analysis of the malware and iOS architecture is important to identify the best mitigation...

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Bibliographic Details
Main Authors: Ahmad, A. (Author), Husainiamer, M.A (Author), Idris, M.Y.I (Author), Mohd Saudi, M. (Author)
Format: Article
Language:English
Published: Springer-Verlag Italia s.r.l. 2023
Subjects:
Online Access:View Fulltext in Publisher
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LEADER 02868nam a2200385Ia 4500
001 10.1007-s11416-023-00477-y
008 230529s2023 CNT 000 0 und d
020 |a 22638733 (ISSN) 
245 1 0 |a iOS mobile malware analysis: a state-of-the-art 
260 0 |b Springer-Verlag Italia s.r.l.  |c 2023 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1007/s11416-023-00477-y 
856 |z View in Scopus  |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159721447&doi=10.1007%2fs11416-023-00477-y&partnerID=40&md5=49c2548922b9ced4cff69dbe34a337ef 
520 3 |a In earlier years, most malware attacks were against Android smartphones. Unfortunately, for the past few years, the trend has shifted towards attacks against the Apple iOS smartphone. Consequently, an in-depth analysis of the malware and iOS architecture is important to identify the best mitigation solution against malware exploitation. Hence, this paper presents a state-of-the-art deep analysis of malware against iOS smartphones. This includes comprehensive studies of malware architecture involving payload, propagation, operating algorithm, infection, and activation with underlying integration with a phylogenetic concept. Phylogenetic, borrowed from the biology field, can identify any evolution of the origin of the malware involved. To support this deep analysis of malware, a preliminary study was conducted using 12 malware samples, by focusing on social media and online banking. This took place in a controlled laboratory using hybrid analysis. The result showed that there is a way to identify the evolution of malware and as a result, a model has been developed. Based on the evaluation, 4% of mobile applications matched the patterns developed in this model. This proves that the model developed in this paper can detect any possible security exploitation related to social media and online banking for iOS mobile applications. This work can be used as guidance for other researchers working on similar interests in the future. © 2023, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature. 
650 0 4 |a Hybrid analysis 
650 0 4 |a iOS exploitation 
650 0 4 |a IOS exploitation 
650 0 4 |a Malware 
650 0 4 |a Malware classification 
650 0 4 |a Malware classifications 
650 0 4 |a Mobile computing 
650 0 4 |a Mobile malware 
650 0 4 |a Online banking 
650 0 4 |a On-line banking 
650 0 4 |a Phylogenetic 
650 0 4 |a Phylogenetics 
650 0 4 |a Smart phones 
650 0 4 |a Smartphones 
650 0 4 |a Social media 
650 0 4 |a Social networking (online) 
650 0 4 |a State of the art 
700 1 0 |a Ahmad, A.  |e author 
700 1 0 |a Husainiamer, M.A.  |e author 
700 1 0 |a Idris, M.Y.I.  |e author 
700 1 0 |a Mohd Saudi, M.  |e author 
773 |t Journal of Computer Virology and Hacking Techniques