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

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

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

Content Provider	Tech Science Press
Author	Raymond, V. Joseph Raj, R. Jeberson Retna Raymond, V. Jeberson, R.
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.
Related Links	https://www.techscience.com/csse/v44n3/49165
Starting Page	2147
Ending Page	2163
ISSN	02676192
DOI	10.32604/csse.2023.028227
Issue Number	3
Journal	Computer Systems Science and Engineering (CSSE)
Volume Number	44
Language	English
Publisher Date	2022-08-01
Access Restriction	Open
Subject Keyword	smart cities hybrid analysis Zero-day exploit supervised learning principal component analysis
Content Type	Text
Resource Type	Article
Subject	Computer Science

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