DroidDetector: Android Malware Characterization and Detection Using Deep Learning

By Zhenlong Yuan, Yongqiang Lu, Yibo Xue. 2016

420 citations recorded by the source corpus.

Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test DroidDetector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. DroidDetector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bfbdf36d50066c39dedf688150d63ba4b84d7e52

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Citations in the corpus

A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “Droid-detector [28], a machine learning based method, is proposed to detect Android malware.”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Authors in [555] usedDBN architecture to construct a deep learningmodel and compared detection accuracy with SVM, C4.”
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019, methodology): “[10] used Deep Belief Network on a combination of static and dynamic features of”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, neutral): “[177] [178] [179] [180] [181] [182] [183] [184] [185] [186] [187] [188]”
A state-of-the-art survey of malware detection approaches using data mining techniques (2018, methodology): “Android malware characterization and detection [40] Deep belief networks Hybrid Google play and android Malware genome project 1860 96.”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[331] Android malware detection Apps in Contagio Mobile, Google Play Store and Genome Project Unsupervised & supervised DBN”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “References [76,77] developed DL-based detectors of malicious Android applications using features from static and dynamic analyses, with the second study improving on the ﬁrst.”