MalDozer: Automatic framework for android malware detection using deep learningAndroid OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in
Traditional Android malware detection methods, such as signature-based methods or methods monitoring battery consumption, may fail to detect recent malware. Therefore, we present a novel method for detecting malware in Android applications using Gated Recurrent Unit (GRU), which is a type of ...
The Android operating system (OS) dominates the mobile phone OS industry, with over 70% of the market share. With the growth of Android OS-based smartphones, it has become a prime target for mobile malware attacks. Minimal alterations in malware samples can easily evade traditional detection ...
The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detect...
The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces a hybrid deep learnin...
Android malware detection using deep learning, contains malware samples, papers, tools etc. Android Malware Sample Android Malware Detection Paper Android Third Markets Android Analysis Tools Releases No releases published Packages No packages published...
Deep learning techniques are also applied to Trojan detection19,28 in conventional cyber attacks. To the best of our knowledge exploiting the Buffer overflow vulnerability in DNA sequencing pipeline using a specially designed DNA was first demonstrated in15. To detect DNA sequences containing the ...
Detecting and Classifying Android Malware using Deep Learning Techniques - Colorado-Mesa-University-Cybersecurity/DeepLearning-AndroidMalware
This paper presents a systematic review of malware detection using Deep Learning techniques. On the basis of the evolution towards Deep Learning-based techniques, research taxonomy is proposed. Recent techniques for detecting malware on Android, iOS, IoT, Windows, APTs, and Ransomware are also ...
He is currently pursuing an MS degree at the Department of Computer Science, Yonsei University. His current research interests include neural networks, artificial intelligence, and controllable representation learning.Reference (52) J.Y. Kim et al. Zero-day Malware Detection using Transferred Generative...