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Intelligent Android Malware Detection in Real-World Environments Using Machine and Deep Learning Model
ID：149 View protection：Participant Only Updated time：2025-12-23 13:19:08 Views：101 In-person

Start Time：2025-12-29 14:15

Duration：15min

Session：[S3] Track 3: Privacy, Security for Networks [S3] Track 3: Privacy, Security for Networks

Slide
Abstract
This paper proposes a novel algorithm to detect the Android malware. The algorithm implements several models of machine and deep learning. The proposed method uses TUANDROMD to develop and assess these models; six models were implemented; namely, Gradient Boosting, Random Forest, Support Vector Machine, XGBoost, Multi-Layer Perceptron, and a custom deep learning model developed in PyTorch. The Random Forest classifier has the highest performance, with an AUC of 1.00 indicating that the algorithm is able to accurately classify samples with almost no errors, while the other models demonstrated strong predictive capabilities. In addition, the ROC curve indicates excellent performance in distinguishing between malicious and safe applications. These results give a good indication that machine learning and deep learning are robust in Android malware detection. This work contributes to the development of reliable, data-driven security solutions capable of addressing the evolving challenges in mobile threat detection.
 
Keywords
Android malware, machine learning, malware detection, Mobile security, deep learning, TUANDROMD dataset, cybersecurity
Speaker
Adnan Al-Smadi
Zarqa University

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Important Dates

  • Conference date

    12-29

    2025

    -

    12-31

    2025

  • 12-30 2025

    Presentation submission deadline

  • 02-10 2026

    Draft paper submission deadline

  • 02-10 2026

    Registration deadline

Sponsored By

United Societies of Science

Organized By

扎尔卡大学

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