Mobile malware attacks: Review, taxonomy & future directions

A Qamar, A Karim, V Chang - Future Generation Computer Systems, 2019 - Elsevier
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 …

Comprehensive review and analysis of anti-malware apps for smartphones

M Talal, AA Zaidan, BB Zaidan, OS Albahri… - Telecommunication …, 2019 - Springer
The new and disruptive technologies for ensuring smartphone security are very limited and
largely scattered. The available options and gaps in this research area must be analysed to …

Towards a network-based framework for android malware detection and characterization

AH Lashkari, AFA Kadir, H Gonzalez… - 2017 15th Annual …, 2017 - ieeexplore.ieee.org
Mobile malware is so pernicious and on the rise, accordingly having a fast and reliable
detection system is necessary for the users. In this research, a new detection and …

Constructing features for detecting android malicious applications: issues, taxonomy and directions

W Wang, M Zhao, Z Gao, G Xu, H **an, Y Li… - IEEE …, 2019 - ieeexplore.ieee.org
The number of applications (apps) available for smart devices or Android based IoT (Internet
of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill …

A two-layer deep learning method for android malware detection using network traffic

J Feng, L Shen, Z Chen, Y Wang, H Li - Ieee Access, 2020 - ieeexplore.ieee.org
Because of the characteristic of openness and flexibility, Android has become the most
popular mobile platform. However, it has also become the most targeted system by mobile …

The dark side (-channel) of mobile devices: A survey on network traffic analysis

M Conti, QQ Li, A Maragno… - … surveys & tutorials, 2018 - ieeexplore.ieee.org
In recent years, mobile devices (eg, smartphones and tablets) have met an increasing
commercial success and have become a fundamental element of the everyday life for …

A survey for user behavior analysis based on machine learning techniques: current models and applications

A G. Martín, A Fernández-Isabel, I Martín de Diego… - Applied …, 2021 - Springer
Significant research has been carried out in the field of User Behavior Analysis, focused on
understanding, modeling and predicting past, present and future behaviors of users …

[PDF][PDF] A Lightweight IoT Cryptojacking Detection Mechanism in Heterogeneous Smart Home Networks.

E Tekiner, A Acar, AS Uluagac - NDSS, 2022 - ndss-symposium.org
Recently, cryptojacking malware has become an easy way of reaching and profiting from a
large number of victims. Prior works studied the cryptojacking detection systems focusing on …

CNN-based android malware detection

M Ganesh, P Pednekar, P Prabhuswamy… - … on software security …, 2017 - ieeexplore.ieee.org
The growth in mobile devices has exponentially increased, making information easy to
access but at the same time vulnerable. Malicious applications can gain access to sensitive …

Android malware detection using network traffic based on sequential deep learning models

S Fallah, AJ Bidgoly - Software: Practice and experience, 2022 - Wiley Online Library
The increasing trend of smartphone capabilities has caught the attention of many users. This
has led to the emergence of malware that threatening the users' privacy and security. Many …