2023
doi: 10.12720/jait.14.4.838-845
Deep Image: An Efficient Image-Based Deep Conventional Neural Network Method for Android Malware Detection
Marwa A. Marzouk1,*
,
Mohamed Elkholy2
- 1Information Technology Department, Matrouh University, Marsa Matrouh, Egypt
- 2Computer Engineering Department, Pharos University in Alexandria, Alexandria, Egypt
- Manuscript receivedDecember 17, 2022
- revisedFebruary 28, 2023
- acceptedMay 22, 2023
- publishedAugust 22, 2023
Abstract
The continuous increment of malware and its complexity motivated researchers to implement techniques to detect and classify it. Manual detection of malicious files is time consuming and shows poor results. Recently, Deep Convolution Neural Networks (DCNN) shows promising results in malware detection. DCNNs include large number of fully connected layers that are capable to deal with fast iterations of Android malware. Compared to the existing approach, DCNN shows high performance and accuracy in detecting different types of malwares. The proposed work combines Scale-Invariant Feature Transform (SIFT) and DCNN to detect malware features. Combining SIFT with DCNN allow higher accuracy of features classification and overcome the problem of single-feature extraction. The proposed method is compared to existing approaches to malware detection in terms of anticipated time and detection accuracy. The experimental results showed the significant enhancement offered by the proposed work in terms of accuracy and performance.
Keywords
- malware
- Deep Convolution Neural Networks (DCNN)
- Scale-Invariant Feature Transform (SIFT)
- color image transformation
How to Cite
Marwa A. Marzouk and Mohamed Elkholy, "Deep Image: An Efficient Image-Based Deep Conventional Neural Network Method for Android Malware Detection," Journal of Advances in Information Technology, vol. 14, no. 4, pp. 838-845, 2023. https://doi.org/10.12720/jait.14.4.838-845
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
