We design a deep learning based hybrid analysis technique, which combines the complementary strengths of each analysis paradigm to attain better accuracy.
Abstract—The phenomenal growth in use of android devices in the recent years has also been accompanied by the rise of android malware.
Sep 28, 2021 · CONCLUSION AND FUTURE WORK. In this work, we used API-calls and system calls to train deep learning models for security vetting of Android apps.
A system that uses deep learning with both static and dynamic analysis will help mitigate much of the problem that limits the traditional approach of android ...
To address the above issues, we design a deep learning based hybrid analysis technique, which com- bines the complementary strengths of each analysis paradigm ...
In the Derbine dataset, the proposed hybrid model reached an accuracy of 0.9828%, which is an excellent performance. The highest accuracy obtained in ...
Dec 25, 2021 · This project investigates how to apply big-data analysis techniques to analyze mobile apps for the Android platform, for the purpose of ...
Jul 27, 2024 · This paper aims to investigate recent advances in malware detection on MacOS, Windows, iOS, Android, and Linux using deep learning (DL)
Hybrid analysis of android apps for security vetting using deep learning. In 2020 IEEE Conference on Communications and Network Security (CNS'20). IEEE, 1–9 ...
People also ask
What is hybrid analysis in cyber security?
Which is the framework for malware analysis in Android?
What is BFEDroid a feature selection technique to detect malware in Android apps using machine learning?
Jul 29, 2022 · We propose a risk estimation approach based on an analysis of the app's code. This analysis adopts a hybrid strategy, exploiting static and dynamic code ...