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EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning
The Android operating system has become the most popular operating syste...
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Android Malware Characterization using Metadata and Machine Learning Techniques
Android Malware has emerged as a consequence of the increasing popularit...
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A Review on The Use of Deep Learning in Android Malware Detection
Android is the predominant mobile operating system for the past few year...
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A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion
In recent years, malware becomes more threatening. Concerning the increa...
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DeepMAL – Deep Learning Models for Malware Traffic Detection and Classification
Robust network security systems are essential to prevent and mitigate th...
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N-gram Opcode Analysis for Android Malware Detection
Android malware has been on the rise in recent years due to the increasi...
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Android Malware Detection using Large-scale Network Representation Learning
With the growth of mobile devices and applications, the number of malici...
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DL-Droid: Deep learning based android malware detection using real devices
The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8 dynamic features only) and 99.6 features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.
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