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 develo** rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

A review on challenges and future research directions for machine learning-based intrusion detection system

A Thakkar, R Lohiya - Archives of Computational Methods in Engineering, 2023 - Springer
Research in the field of Intrusion Detection is focused on develo** an efficient strategy that
can identify network attacks. One of the important strategies is to supervise the network …

[HTML][HTML] MalDozer: Automatic framework for android malware detection using deep learning

EMB Karbab, M Debbabi, A Derhab, D Mouheb - Digital investigation, 2018 - Elsevier
Android OS experiences a blazing popularity since the last few years. This predominant
platform has established itself not only in the mobile world but also in the Internet of Things …

Deeprefiner: Multi-layer android malware detection system applying deep neural networks

K Xu, Y Li, RH Deng, K Chen - 2018 IEEE European …, 2018 - ieeexplore.ieee.org
As malicious behaviors vary significantly across mobile malware, it is challenging to detect
malware both efficiently and effectively. Also due to the continuous evolution of malicious …

Application domains, evaluation data sets, and research challenges of IoT: A systematic review

R Lohiya, A Thakkar - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
We are at the brink of Internet of Things (IoT) era where smart devices and other wireless
devices are redesigning our environment to make it more correlative, flexible, 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 taxonomy and qualitative comparison of program analysis techniques for security assessment of android software

A Sadeghi, H Bagheri, J Garcia… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
In parallel with the meteoric rise of mobile software, we are witnessing an alarming
escalation in the number and sophistication of the security threats targeted at mobile …

FSDroid:-A feature selection technique to detect malware from Android using Machine Learning Techniques: FSDroid

A Mahindru, AL Sangal - Multimedia Tools and Applications, 2021 - Springer
With the recognition of free apps, Android has become the most widely used smartphone
operating system these days and it naturally invited cyber-criminals to build malware …

Hybrid Android malware detection: A Review of heuristic-based approach

RA Yunmar, SS Kusumawardani, F Mohsen - IEEE Access, 2024 - ieeexplore.ieee.org
Over the last decade, numerous research efforts have been dedicated to countering
malicious mobile applications. Given its market share, Android OS has been the primary …

A review on android malware: Attacks, countermeasures and challenges ahead

SG Selvaganapathy… - Journal of Cyber …, 2021 - journals.riverpublishers.com
Smartphones usage have become ubiquitous in modern life serving as a double-edged
sword with opportunities and challenges in it. Along with the benefits, smartphones also …