May 22, 2020 · This work raises a stacking ensemble framework SEDMDroid to identify Android malware. Specifically, to ensure individual's diversity, it adopts random feature ...

Promising experiment results reveal that the proposed method is an effective way to identify Android malware. Index Terms—Android security, Deep learning, ...

To fight against the explosive growth of Android malware, we propose a static malware detection framework, known as SEDMDroid. Malware presents a serious threat ...

In such context, by studying the actions of malware, this work develops a novel framework SEDMDroid to detect Android malware, based on an enhanced stacking ...

Sep 9, 2024 · In summary, the proposed stacking ensemble framework significantly enhances the detection of Android malware, making it a valuable tool in the ...

Research and analysis Android smart phone Trojan horses can provide corresponding technical support for malware detection on Android smart phones, which has ...

Jul 28, 2023 · ABSTRACT Suggest a architecture that enables the use of various machine learning algorithms to effectively differentiate between malware files ...

Mar 28, 2022 · Zhu et al. [18] proposed a stacking integration framework, SEDMDroid, to identify Android malware. Principal component analysis was performed on ...

Oct 25, 2021 · SEDMDroid An enhanced stacking ensemble framework for Android malware detection IEEE PROJECTS 2021-2022 TITLE LIST MTech,BTech,BE,ME,B.Sc ...

This study addresses this imperative by introducing an ensemble stacking framework, leveraging various classifiers to enhance the efficiency of Android malware.