Abstract: Malware has become more harmful than in the past as the number of intelligent systems and Internet-connected devices increased dramatically.
We present a novel method for detecting malware in Android applications using Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN).
This paper proposes an android mobile malware detection system based on deep neural network, a novel malware detection method which uses optimized deep ...
Jul 27, 2024 · This paper aims to investigate recent advances in malware detection on MacOS, Windows, iOS, Android, and Linux using deep learning (DL)
AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature ...
In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation.
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An Android malware detection system that applies deep learning technique to face the threats of Android malware and reaches an accuracy of 95.31% is ...
In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN).
Sep 9, 2024 · In this paper we use the deep learning based Long short-term memory (LSTM) network for android malware classification. The model is effective in ...
This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions.
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