Mobile malware attacks: Review, taxonomy & future directions

Abstract

A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent years. Android's popular and attractive environment not only captured the attention of users but also increased security concerns. As a result, Android malware detection is one of the sizzling topics in the mobile security domain. This paper provides a comprehensive review of state-of-the-art mobile malware attacks, vulnerabilities, detection techniques and security solutions over the period of 2013-2019 that majorly targeted Android platform. We have presented various well-organized and in-depth taxonomies that uncover mobile malware detection approaches based on their analysis techniques, working platform, data acquisition, operational impact, obtained results and artificial intelligence component involved. Another taxonomy comprises of mobile malware attack vector is presented to look threat clusters and loopholes to locate their malicious widespread impact on communities. Furthermore, we have discussed and classified forensic analysis efforts in mobile malware detection perspective. From intruder point of view, we have compared various evasion techniques that are used prominently by the malware authors to hinder detection efforts. Finally, future work directions are presented as guidelines for academia and industry alike to help them reduce or even avoid the harmful impact of these annoying efforts.

PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting

“…The dynamic analysis focuses mainly on running time and interactive features of apps, therefore it is more complex and slower than the static analysis. The usability of dynamic analysis, in fact, lies in the ability to detect "obfuscated" and "polymorphic" malware which mostly escapes under static analysis [4]. Finding optimum running time for dynamic analysis is also difficult, as the start of malicious activities can vary from sample to sample [32].…”

“…Finding optimum running time for dynamic analysis is also difficult, as the start of malicious activities can vary from sample to sample [32]. The dynamic features are extracted through system calls, network activities, user interaction, Central Processing Unit (CPU) consumption, and other phone activities [4]. Table 3 lists and summarizes some of the popular works related to dynamic analysis using dynamic features and ML-based Android Malware detection (Input/Output (I/O), Read/Write (R/W)).…”

“…These techniques also demand high computational resources that burden the end-user device. To address these limitations and bottlenecks, non-signature-based techniques have been extensively used in recent decades [4].…”

Truva Atı Zararlı Yazılımlarına Yaklaşım ve Çözüm Önerileri

“…Zararlı yazılım saldırıları hedefli veya hedefsiz olmak üzere iki çeşit yöntemle tasarlanmaktadır. Hedefsiz otomatik siber saldırılar çoğunlukla web tabanlı olduğundan kullanıcıyla etkileşimine gerek duymaktadırlar [Man-in-the-Browser gibi] (Öztürk, 2018;Qamar, Karim, & Chang, 2019;Vinay, & Kok, 2018;Jain, vd. 2014).…”

A Game Theoretical Model for Anticipating Email Spear-Phishing Strategies

“…Companies acquire network protection solutions (IDS, firewalls, honeypots etc..) to mitigate spear-phishing intrusion [24,25]. At the employee level, they opt for antiviruses [49,50] or filters based on black and white lists installed on browsers [20][21][22][23]. Training sessions with tools simulating real attacks are planned and educational games [16][17][18][19] set up for this purpose are used in short or long term.…”