A review of android malware detection approaches based on machine learning

K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are developing rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

The rise of “blockchain”: bibliometric analysis of blockchain study

A Firdaus, MFA Razak, A Feizollah, IAT Hashem… - Scientometrics, 2019 - Springer
The blockchain is a technology which accumulates and compiles data into a chain of
multiple blocks. Many blockchain researchers are adopting it in multiple areas. However …

Malware analysis and detection using machine learning algorithms

MS Akhtar, T Feng - Symmetry, 2022 - mdpi.com
One of the most significant issues facing internet users nowadays is malware. Polymorphic
malware is a new type of malicious software that is more adaptable than previous …

A systematic literature review of android malware detection using static analysis

Y Pan, X Ge, C Fang, Y Fan - IEEE Access, 2020 - ieeexplore.ieee.org
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 …

Malicious application detection in android—a systematic literature review

T Sharma, D Rattan - Computer Science Review, 2021 - Elsevier
Context: In last decade, due to tremendous usage of smart phones it seems that these
gadgets became an essential necessity of day-to-day life. People are using new …

Paired: An explainable lightweight android malware detection system

MM Alani, AI Awad - IEEE Access, 2022 - ieeexplore.ieee.org
With approximately 2 billion active devices, the Android operating system tops all other
operating systems in terms of the number of devices using it. Android has gained wide …

[HTML][HTML] DL-AMDet: Deep learning-based malware detector for android

AR Nasser, AM Hasan, AJ Humaidi - Intelligent Systems with Applications, 2024 - Elsevier
The Android operating system, with its market share leadership and open-source nature in
smartphones, has become the primary target of malware. However, detecting malicious …

Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection

MA Rahman, AT Asyhari, OW Wen, H Ajra… - Multimedia Tools and …, 2021 - Springer
The rapid advancement of technologies has enabled businesses to carryout their activities
seamlessly and revolutionised communications across the globe. There is a significant …

Android malware detection using genetic algorithm based optimized feature selection and machine learning

A Fatima, R Maurya, MK Dutta… - … and signal processing …, 2019 - ieeexplore.ieee.org
Android platform due to open source characteristic and Google backing has the largest
global market share. Being the world's most popular operating system, it has drawn the …

Android malware detection using machine learning with feature selection based on the genetic algorithm

J Lee, H Jang, S Ha, Y Yoon - Mathematics, 2021 - mdpi.com
Since the discovery that machine learning can be used to effectively detect Android
malware, many studies on machine learning-based malware detection techniques have …