[HTML][HTML] MFDroid: A stacking ensemble learning framework for Android malware detection

X Wang, L Zhang, K Zhao, X Ding, M Yu - Sensors, 2022 - mdpi.com
As Android is a popular a mobile operating system, Android malware is on the rise, which
poses a great threat to user privacy and security. Considering the poor detection effects of …

A novel dynamic android malware detection system with ensemble learning

P Feng, J Ma, C Sun, X Xu, Y Ma - IEEE Access, 2018 - ieeexplore.ieee.org
With the popularity of Android smartphones, malicious applications targeted Android
platform have explosively increased. Proposing effective Android malware detection method …

SEDMDroid: An enhanced stacking ensemble framework for Android malware detection

H Zhu, Y Li, R Li, J Li, Z You… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The popularity of the Android platform in smartphones and other Internet-of-Things devices
has resulted in the explosive of malware attacks against it. Malware presents a serious …

Mlifdect: android malware detection based on parallel machine learning and information fusion

X Wang, D Zhang, X Su, W Li - Security and Communication …, 2017 - Wiley Online Library
In recent years, Android malware has continued to grow at an alarming rate. More recent
malicious apps' employing highly sophisticated detection avoidance techniques makes the …

JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters

L Cai, Y Li, Z Xiong - Computers & Security, 2021 - Elsevier
Android malware detection is an important problem that must be urgently studied and
solved. Machine learning-based methods first extract features from applications and then …

Comprehensive android malware detection based on federated learning architecture

W Fang, J He, W Li, X Lan, Y Chen, T Li… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Android malware and its variants are a major challenge for mobile platforms. However, there
are two main problems in the existing detection methods:) The detection method lacks the …

DroidEncoder: Malware detection using auto-encoder based feature extractor and machine learning algorithms

H Bakır, R Bakır - Computers and Electrical Engineering, 2023 - Elsevier
Android Malware detection became a hot topic over the last several years. Although
considerable studies have been conducted utilizing machine learning-based methods, little …

A novel permission-based Android malware detection system using feature selection based on linear regression

DÖ Şahin, OE Kural, S Akleylek, E Kılıç - Neural Computing and …, 2023 - Springer
With the developments in mobile and wireless technology, mobile devices have become an
important part of our lives. While Android is the leading operating system in market share, it …

[HTML][HTML] Android malware detection method based on highly distinguishable static features and DenseNet

J Yang, Z Zhang, H Zhang, JW Fan - Plos one, 2022 - journals.plos.org
The rapid growth of malware has become a serious problem that threatens the security of
the mobile ecosystem and needs to be studied and resolved. Android is the main target of …

Evaluating machine learning models for Android malware detection: A comparison study

MS Rana, C Gudla, AH Sung - Proceedings of the 2018 VII International …, 2018 - dl.acm.org
Android is the most popular mobile operating system having billions of active users
worldwide that attracted advertisers, hackers, and cybercriminals to develop malware for …