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ARTICLE

Optimal Unification of Static and Dynamic Features for Smartphone Security Analysis

Sumit Kumar1,*, S. Indu2, Gurjit Singh Walia1

1 Scientific Analysis Group, DRDO, Delhi, 110054, India
2 Delhi Technological University, Delhi, 110042, India

* Corresponding Author: Sumit Kumar. Email: email

Intelligent Automation & Soft Computing 2023, 35(1), 1035-1051. https://doi.org/10.32604/iasc.2023.024469

Abstract

Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification model to distinguish benign and malicious applications. The suggested framework is extensively experimentally validated through both qualitative and quantitative analysis and results are compared with the existing solutions. Performance evaluation over benchmarked datasets from Google Play Store, Drebin, Androzoo, AMD, and CICMalDroid2020 revealed that the suggested solution outperforms state-of-the-art methods. We achieve average detection accuracy of 98.62% and F1 Score of 0.9916.

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Cite This Article

S. Kumar, S. Indu and G. Singh Walia, "Optimal unification of static and dynamic features for smartphone security analysis," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 1035–1051, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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