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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, Guoai Xu, Dawei Sun
exaly   +4 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, Alice Li, Daniel Rodríguez, Oriana Riva, T. Lillicrap   +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, Yuanchun Li, Guohong Liu, Shan-Hui Zhao, Tao Yu, Toby Jia-Jun Li, Shiqi Jiang, Yunhao Liu, Yaqin Zhang, Yunxin Liu   +9 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 However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps.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, Zhoujie Ding, David A. Wagner   +2 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, B. Issac, Seibu Mary Jacob, Longzhi Yang   +3 more
semanticscholar   +1 more source

Using Machine Learning to Identify Android Malware Relying on API calling sequences and Permissions [PDF]

Journal of Computing and Communication, 2022
The revolutionary in cyber attacks, especially in smartphones are rising. The Android operating system is becoming one of the most leading operating systems. Therefore, Android malware is rising in terms of popularity.
Haytham Metwaie, Omar Shereef, Amr Ehab, Mostafa Ashaf, Seif ElDein Mohamed, Eslam Amer, Ammar Mohamed   +6 more
doaj   +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 Rahima Manzil, S. M. Naik   +1 more
semanticscholar   +1 more source

ATVHunter: Reliable Version Detection of Third-Party Libraries for Vulnerability Identification in Android Applications [PDF]

International Conference on Software Engineering, 2021
Third-party libraries (TPLs) as essential parts in the mobile ecosystem have become one of the most significant contributors to the huge success of Android, which facilitate the fast development of Android applications.
Xian Zhan, Lingling Fan, Sen Chen, Feng Wu, Tianming Liu, Xia-Pu Luo, Yang Liu   +6 more
semanticscholar   +1 more source

Android Malware Detection Based on Hypergraph Neural Networks

Applied Sciences, 2023
Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant.
Dehua Zhang, Xiangbo Wu, Erlu He, Xiaobo Guo, Xiaopeng Yang, Ruibo Li, Hao Li   +6 more
doaj   +1 more source

ROBOTIC SYSTEMS FOR SPECIAL AND MEDICAL INTELLIGENT ASSISTANCE

Наука и инновации в медицине, 2016
Exoskeletons gradually come to all spheres of human activities - from building houses to sports, from medicine to military usage. Exoskeleton complexes are rapidly developing with the help of 3D printing, microwires, new sources of energy and computing ...
Е А Dudorov, А А Bogdanov, A F Permyakov   +2 more
doaj   +1 more source