Hybrid classification of Android malware based on fuzzy clustering and the gradient boosting machine

AA Taha, SJ Malebary - Neural Computing and Applications, 2021 - Springer
The widespread use of smartphones in recent years has led to a significant rise in the
sophistication and number of Android malicious applications (apps) targeting smartphone …

[PDF][PDF] Intelligent hybrid approach for Android malware detection based on permissions and API calls

A Altaher, OM Barukab - … Journal of Advanced Computer Science and …, 2017 - academia.edu
Android malware is rapidly becoming a potential threat to users. The number of Android
malware is growing exponentially; they become significantly sophisticated and cause …

Android malware classification based on ANFIS with fuzzy c-means clustering using significant application permissions

A Altaher, O Barukab - Turkish Journal of Electrical …, 2017 - journals.tubitak.gov.tr
Mobile phones have become an essential part of our lives because we depend on them to
perform many tasks, and they contain personal and important information. The continuous …

Android malicious application classification using clustering

H Rathore, SK Sahay, P Chaturvedi… - Intelligent Systems Design …, 2020 - Springer
Android malware have been growing at an exponential pace and becomes a serious threat
to mobile users. It appears that most of the anti-malware still relies on the signature-based …

A novel approach for mobile malware classification and detection in Android systems

Q Zhou, F Feng, Z Shen, R Zhou, MY Hsieh… - Multimedia Tools and …, 2019 - Springer
With the increasing number of malicious attacks, the way how to detect malicious Apps has
drawn attention in mobile technology market. In this paper, we proposed a detection model …

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 …

An ensemble approach based on fuzzy logic using machine learning classifiers for android malware detection

İ Atacak - Applied Sciences, 2023 - mdpi.com
In this study, a fuzzy logic-based dynamic ensemble (FL-BDE) model was proposed to
detect malware exposed to the Android operating system. The FL-BDE model contains a …

A new wrapper-based feature selection technique with fireworks algorithm for android malware detection

M Guendouz, A Amine - International Journal of Software Science …, 2022 - igi-global.com
Smartphone use has expanded dramatically in recent years, particularly for Android-based
smartphones, due to their wide availability and competitive pricing compared to non-Android …

Android malware detection based on factorization machine

C Li, K Mills, D Niu, R Zhu, H Zhang, H Kinawi - IEEE Access, 2019 - ieeexplore.ieee.org
As the popularity of Android smart phones has increased in recent years, so too has the
number of malicious applications. Due to the potential for data theft that mobile phone users …

[PDF][PDF] Intelligent approach for android malware detection

S Abdulla, A Altaher - KSII Transactions on Internet and Information …, 2015 - koreascience.kr
As the Android operating system has become a key target for malware authors, Android
protection has become a thriving research area. Beside the proved importance of system …