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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 +5 more
doaj +2 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
Android in the Wild: A Large-Scale Dataset for Android Device Control [PDF]
Neural Information Processing Systems, 2023 There is a growing interest in device-control systems that can interpret human natural language instructions and execute them on a digital device by directly controlling its user interface. We present a dataset for device-control research, Android in the
Christopher Rawles +4 more
semanticscholar +1 more source
AutoDroid: LLM-powered Task Automation in Android [PDF]
ACM/IEEE International Conference on Mobile Computing and Networking, 2023 Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones. However, existing approaches suffer from poor scalability due to the limited language understanding ability and the non ...
Hao Wen +9 more
semanticscholar +1 more source
IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense [PDF]
IEEE Internet of Things Journal, 2023 Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from the ...
Rahul Yumlembam +3 more
semanticscholar +1 more source
Continuous Learning for Android Malware Detection [PDF]
USENIX Security Symposium, 2023 Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps.
Yizheng Chen +2 more
semanticscholar +1 more source
Applied Sciences, 2023 Since the development of information systems during the last decade, cybersecurity has become a critical concern for many groups, organizations, and institutions.
Ashwag Albakri +4 more
semanticscholar +1 more source
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 ...
Hashida Haidros +2 more
semanticscholar +1 more source
Deep Learning for Android Malware Defenses: A Systematic Literature Review [PDF]
ACM Computing Surveys, 2021 Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given
Yue Liu +3 more
semanticscholar +1 more source
Malware Analysis in IoT & Android Systems with Defensive Mechanism
Electronics, 2022 The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology.
Chandra Shekhar Yadav +8 more
semanticscholar +1 more source