Convolutional Neural Network for Classification of Android Applications Represented as Grayscale Images
Meghna Dhalaria1, Ekta Gandotra2
1Meghna Dhalaria, Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan (Himachal Pradesh) India.
2Ekta Gandotra, Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan (Himachal Pradesh) India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 24 October 2019 | Manuscript Published on 26 December 2019 | PP: 835-843 | Volume-8 Issue-12S October 2019 | Retrieval Number: L118910812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1189.10812S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A rapid dissemination of Android operating system in smart phone market has resulted in an exponential growth of threats to mobile applications. Various studies have been carried out in academia and industry for the identification and classification of malicious applications using machine learning and deep learning algorithms. Convolution Neural Network is a deep learning technique which has gained popularity in speech and image recognition. The conventional solution for identifying Android malware needs learning based on pre-extracted features to preserve high performance for detecting Android malware. In order to reduce the efforts and domain expertise involved in hand-feature engineering, we have generated the grayscale images of AndroidManifest.xml and classes. dex files which are extracted from the Android package and applied Convolution Neural Network for classifying the images. The experiments are conducted on a recent dataset of 1747 malicious Android applications. The results indicate that classes. dex file gives better results as compared to the AndroidManifest.xml and also demonstrate that model performs better as the image become larger.
Keywords: Android Malware, Android Malware Grayscale Images, Convolutional Neural Network, Deep Learning, Feature Engineering, Machine Learning.
Scope of the Article: Classification