Android malware analysis in a nutshell. [PDF]
PLoS ONE, 2022 This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based.
Iman Almomani +2 more
doaj +4 more sources
The rise of obfuscated Android malware and impacts on detection methods [PDF]
PeerJ Computer Science, 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
Wael F. Elsersy +2 more
doaj +3 more sources
FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification [PDF]
Entropy, 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.
Changnan Jiang +3 more
doaj +2 more sources
The Evolution of Android Malware and Android Analysis Techniques [PDF]
ACM Computing Surveys, 2017 With the integration of mobile devices into daily life, smartphones are privy to increasing amounts of sensitive information. Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. This article presents a
Lorenzo Cavallaro +2 more
exaly +5 more sources
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, Dawei Sun
exaly +3 more sources
Android malware category detection using a novel feature vector-based machine learning model
Cybersecurity, 2023 Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android ...
Manohar Naik S +2 more
exaly +2 more sources
GPFinder: Tracking the invisible in Android malware [PDF]
2017 12th International Conference on Malicious and Unwanted Software (MALWARE), 2017 Malicious Android applications use clever techniques to hide their real intents from the user and avoid detection by security tools. They resort to code obfuscation and dynamic loading, or wait for special events on the system like reboot or WiFi activation.
Leslous, Mourad +3 more
openaire +4 more sources
OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning [PDF]
Sensors, 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.
Weina Niu +5 more
doaj +2 more sources
Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles [PDF]
Scientific ReportsWith the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort.
Naif Almakayeel
doaj +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