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Android Malware Detection Using Parallel Machine Learning Classifiers [PDF]

, 2014
Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices ...
Muttik, Igor +7 more
core +2 more sources

Resilient and Scalable Android Malware Fingerprinting and Detection [PDF]

, 2020
Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming.
Karbab, ElMouatez Billah
core

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

IEEE Access
Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets.
Mohammed Maray +5 more
doaj +1 more source

A pragmatic android malware detection procedure

Computers & Security, 2017
Abstract The academic security research community has studied the Android malware detection problem extensively. Machine learning methods proposed in previous work typically achieve high reported detection performance on fixed datasets. Some of them also report reasonably fast prediction times.
Palumbo, Paolo +5 more
openaire +1 more source

Extracting Android Applications Data for Anomaly-based Malware Detection

, 2015
In order to apply any machine learning algorithm or classifier, it is fundamentally important to first and foremost collect relevant features. This is most important in the field of dynamic analysis approach to anomaly malware detection systems. In this
Ume U.A +4 more
core

A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps

Egyptian Informatics Journal
The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware.
Collins Chimeleze +3 more
doaj +1 more source

An Improved Malicious Application Detection in Social Networks (MADSN)

مجلة جامعة الزيتونة, 2021
Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware ...
Nagmden Nasser, Adel Abosdel
doaj

Android Malware Detection Based on Deep Learning: Achievements and Challenges

, 2020
With the prosperous of Android applications, Android malware has been scattered everywhere, which raises the serious security risk to users. On the other hand, the rapid developing of deep learning fires the combat between the two sides of malware ...
Chen, Yi, Zou, Wei, Tang, Di
core +1 more source

MalWhiteout: Reducing Label Errors in Android Malware Detection

, 2023
Machine learning based Android malware detection has attracted a great deal of research work in recent years. A reliable malware dataset is critical to evaluate the effectiveness of malware detection approaches.
Wang, H, Wang, L, Luo, X, Sui, Y
core +1 more source

Android malware detection using random forest algorithm

Proceedings of the Nigerian Society of Physical Sciences
The proliferation of mobile devices and their dependence on the android OS has made them prime targets for cybercriminals, leading to an escalating threat of malware.
Samson Isaac +4 more
doaj +1 more source

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