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All Journal Media Journal of General Computer Science (MJGCS)
Pareza Alam Jusia
Department of Informatics, Dinamika Bangsa University, Jambi, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Media Journal of General Computer Science (MJGCS)

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Android Security: Malware Detection with Convolutional Neural Network and Feature Analysis Sharipuddin; Rafi Septiandi Putra; M. Farhan Aulia; Sayid Achmad Maulana; Pareza Alam Jusia
Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v1i1.7

Abstract

Android is a mobile operating system based on a modified version of the Linux kernel and other open-source tools. Due to its system efficiency and the multitude of features it offers to users, the Android operating system has taken a leading position in the technology market and often attracts the attention of cybercriminals. As malware continues to evolve, traditional methods for detecting Android malware, such as signature-based approaches, may not be sufficient to detect the latest malware threats. Therefore, this research proposes a deep learning algorithm, specifically Convolutional Neural Network (CNN) and Component Analysis (PCA), for feature extraction to enhance the accuracy of Android malware detection. The dataset used in this study is the CICAndMal2017 dataset. Testing results are evaluated using three parameters: accuracy, precision, and recall. Experimental results indicate that our deep learning approach outperforms many other methods with an accuracy of 91%.
Co-Authors M. Farhan Aulia Rafi Septiandi Putra Sayid Achmad Maulana Sharipuddin