Android Malware Detection using Multilayer Autoencoder and Random Forest
Android Malware Detection using Multilayer Autoencoder and Random Forest
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : A. Lakshmanarao, M. Shashi
|DOI : 10.14445/22315381/IJETT-V70I11P227|
How to Cite?
A. Lakshmanarao, M. Shashi, "Android Malware Detection using Multilayer Autoencoder and Random Forest," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 249-257, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P227
One of the most challenging concerns in the world of operating systems and software is the presence of malicious software. The Android operating system is also experiencing the same issues. Because of the significant increase in the refinement of Android malware obfuscation and detection avoidance methods, a significant number of conventional malware investigative techniques have become outdated. The malware detection approach based on earlier signatures is ineffective for detecting unknown threats. In recent years, machine learning and deep learning techniques have proved promising for malware detection. A framework is proposed to extract several features like permissions, opcodes, api packages, system calls, intents, and api calls from Android malware and benign apps and to build a classifier for malware detection using the most suitable machine learning and deep learning algorithms. Based on the performance analysis Random Forest algorithm was identified as the suitable classifier as it produced the highest accuracy on raw input. In order to further improve the accuracy, this paper proposes a cascade of multilayer autoencoder for feature extraction followed by the random forest classifier for Android malware detection. A cascade of an autoencoder and random forest was applied to real-world datasets and achieved an accuracy of 99.1%. The proposed work also individually examines the impact of the six types of features to distinguish malware and benign apps.
Android Malware, Random Forest, Multilayer Autoencoder.
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