Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU. [PDF]
PLoS OneThe rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces
Kauser Sk H, Anu V M.
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A Review of Android Malware Detection Approaches Based on Machine Learning
IEEE Access, 2020 Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from ...
Kaijun Liu +5 more
doaj +3 more sources
A Systematic Literature Review of Android Malware Detection Using Static Analysis
IEEE Access, 2020 Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it ...
Ya Pan +3 more
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EURASIP Journal on Information SecuritySince Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques.
Amarjyoti Pathak +2 more
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PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection. [PDF]
Sci RepThe challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android.
Mahindru A +6 more
europepmc +2 more sources
Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles. [PDF]
Sci RepWith the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort.
Almakayeel N.
europepmc +2 more sources
SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System
IEEE Access, 2018 For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention.
Saba Arshad +5 more
doaj +3 more sources
An In-Depth Review of Machine Learning Based Android Malware Detection
Computers and Security, 2022 It is estimated that around 70% of mobile phone users have an Android device. Due to this popularity, the Android operating system attracts a lot of malware attacks.
Hani Ragab Hassen +2 more
exaly +2 more sources
DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
Applied Sciences, 2023 Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher +4 more
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Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis
InformationMobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis ...
Nahier Aldhafferi
doaj +2 more sources