A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

By Taeguen Kim, Boojoong Kang, Mina Rho, Sakir Sezer, Eul Gyu Im. IEEE Transactions on Information Forensics and Security, 2019

453 citations recorded by the source corpus.

With the widespread use of smartphones, the number of malware has been increasing exponentially. Among smart devices, android devices are the most targeted devices by malware because of their high popularity. This paper proposes a novel framework for android malware detection. Our framework uses various kinds of features to reflect the properties of android applications from various aspects, and the features are refined using our existence-based or similarity-based feature extraction method for effective feature representation on malware detection. Besides, a multimodal deep learning method is proposed to be used as a malware detection model. This paper is the first study of the multimodal deep learning to be used in the android malware detection. With our detection model, it was possible to maximize the benefits of encompassing multiple feature types. To evaluate the performance, we carried out various experiments with a total of 41 260 samples. We compared the accuracy of our model with that of other deep neural network models. Furthermore, we evaluated our framework in various aspects including the efficiency in model updates, the usefulness of diverse features, and our feature representation method. In addition, we compared the performance of our framework with those of other existing methods including deep learning-based methods.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eb49737539bc0e3dcebff15f8de3779e4f5b5a6c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jun Zhang
2. Surya Nepal
3. Prabaharan Poornachandran
4. R. Vinayakumar
5. Yang Xiang

In Software

1. Surya Nepal
2. Jun Zhang
3. Yang Xiang

In Signal Processing

1. Wei Luo
2. Surya Nepal
3. Lei Pan
4. Junyang Qiu
5. Prabaharan Poornachandran
6. Jun Zhang
7. R. Vinayakumar
8. Yang Xiang

Citations in the corpus

• Deep Learning Approach for Intelligent Intrusion Detection System (2019, background): “Overall, it was concluded that the hybrid system enhances the performance with the advantage of combining both the misuse and anomaly detection approaches [22], [23], [72].”
• A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “A multimodal deep neural network–based model together with multiple feature types of Android apps was proposed in Reference [122].”