Results 11 to 20 of about 16,685 (199)
A Review of Android Malware Detection Approaches Based on Machine Learning
IEEE Access, 2020 Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from ...
Kaijun Liu, Guoai Xu
exaly +3 more sources
SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System [PDF]
IEEE Access, 2018 © 2013 IEEE. For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention.
Arshad, S. +5 more
core +3 more sources
The rise of obfuscated Android malware and impacts on detection methods. [PDF]
PeerJ Comput Sci, 2022 The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious
Elsersy WF, Feizollah A, Anuar NB.
europepmc +3 more sources
FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification. [PDF]
Entropy (Basel), 2022 With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot.
Jiang C, Yin K, Xia C, Huang W.
europepmc +2 more sources
Android Malware Category and Family Identification Using Parallel Machine Learning [PDF]
Journal of Information Technology Management, 2022 Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.
Ahmed Hashem El Fiky +2 more
doaj +1 more source
Adaptive secure malware efficient machine learning algorithm for healthcare data
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed +8 more
wiley +1 more source
Detection and Prevention of Malware in Android Operating System
Mehran University Research Journal of Engineering and Technology, 2021 The Internet is not safe anymore, malware can be discovered anywhere on the Internet. The risk of malware has increased also due to the increasing popularity and use of Smartphones and their underlying cost-free applications. With its great market share,
Kashif Ali Dahri +2 more
doaj +1 more source
Applied Sciences, 2022 As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware.
Abimbola G. Akintola +9 more
doaj +1 more source
DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis
Applied Sciences, 2020 Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of ...
Xin Su +5 more
doaj +1 more source
OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning. [PDF]
Sensors (Basel), 2020 Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them.
Niu W +5 more
europepmc +2 more sources