Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges | IEEE Journals & Magazine | IEEE Xplore

Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges


We compare and analyze the latest research results of Android malware detection based on modern learning from the aspects of contribution, limitation and prospect, and su...

Abstract:

Advancements in microelectronics have increased the popularity of mobile devices like cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, br...Show More

Abstract:

Advancements in microelectronics have increased the popularity of mobile devices like cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad device support, customizability, and integration with the Google ecosystem, has become the leading operating system for mobile devices. While Android’s openness brings benefits, it has downsides like a lack of official support, fragmentation, complexity, and security risks if not maintained. Malware exploits these vulnerabilities for unauthorized actions and data theft. To enhance device security, static and dynamic analysis techniques can be employed. However, current attackers are becoming increasingly sophisticated, and they are employing packaging, code obfuscation, and encryption techniques to evade detection models. Researchers prefer flexible artificial intelligence methods, particularly deep learning models, for detecting and classifying malware on Android systems. In this survey study, a detailed literature review was conducted to investigate and analyze how deep learning approaches have been applied to malware detection on Android systems. The study also provides an overview of the Android architecture, datasets used for deep learning-based detection, and open issues that will be studied in the future.
We compare and analyze the latest research results of Android malware detection based on modern learning from the aspects of contribution, limitation and prospect, and su...
Published in: IEEE Access ( Volume: 11)
Page(s): 123314 - 123334
Date of Publication: 10 October 2023
Electronic ISSN: 2169-3536

Funding Agency:


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