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A novel efficient optimized machine learning approach to detect malware activities in android applications

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Abstract

The Android device convenience has increased the number of malware developers handling several unknown applications. Machine Learning (ML) approaches help to detect these malicious applications. In this research, a novel ML approach, namely the African Buffalo-based Decision Tree (ABDT) algorithm is developed for detecting malware activities in Android applications. Initially, the dataset is trained to the system that involves Android applications and malware functions. Subsequently, the developed ABDT mechanism is processed on the dataset, and the malware in each application is detected. Additionally, the applications are analyzed based on a static and dynamic manner to detect the malware. Moreover, the developed model is simulated in the network simulator 2, and the performance metrics are calculated. Here, the key novelty of this present research is enabling the monitoring mechanism in the decision with the help of buffalo fitness. Also, the present model was efficient in finding the malicious and unknown apps that have prevented the security threat and system damage. Finally, the attained outcomes are validated with the results of several existing works in terms of accuracy, precision, recall, detection rate, F-measure, and error rate. The presented model has earned 99.85% accuracy, 99.76% precision, 99.83% recall, and 99.79% F-measure.

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Authors and Affiliations

  1. School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

    Praveen Kumar Kaithal & Varsha Sharma

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  1. Praveen Kumar Kaithal
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  2. Varsha Sharma
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Correspondence to Praveen Kumar Kaithal.

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Kaithal, P.K., Sharma, V. A novel efficient optimized machine learning approach to detect malware activities in android applications. Multimed Tools Appl 82, 42833–42850 (2023). https://doi.org/10.1007/s11042-023-15264-6

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