Open Access

ARTICLE

Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

by V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

1 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, Tamilnadu, India
2 School of Computing, SRM Institute of Science and Technology, Chennai, 603203, Tamilnadu, India

* Corresponding Author: V. Joseph Raymond. Email:

Computer Systems Science and Engineering 2023, 44(3), 2147-2163. https://doi.org/10.32604/csse.2023.028227

Received 05 February 2022; Accepted 30 March 2022; Issue published 01 August 2022

Abstract

Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Multicollinearity problem is one of the drawbacks in the existing system. In the proposed work, a novel PCA (Principal Component Analysis) based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach. The Android Sensitive Permission is one major key point to be considered while detecting malware. We select vulnerable columns based on features like sensitive permissions, application program interface calls, services requested through the kernel, and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign. The final goal of this paper is to check benchmarking datasets collected from various repositories like virus share, Github, and the Canadian Institute of cyber security, compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

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Keywords

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