Presentation Details
Intelligent Android Malware Detection in Real-World Environments Using Machine and Deep Learning Model
ID：149
View Protection：ATTENDEE
Updated Time：2025-12-23 13:19:08 Hits：356
In-person
Start Time：2025-12-29 14:15（Asia/Amman）
Duration：15min
Session：S3 Track 3: Privacy, Security for Networks »
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
Professor Zarqa UniversitySubmission Author
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Important Date
-
Conference Date
Dec 29
2025
toDec 31
2025
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Dec 20 2025
Draft paper submission deadline
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Dec 31 2025
Contribution Submission Deadline
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Dec 31 2025
Registration deadline
Sponsored By
United Societies of Science
Organized By
Zarqa University
Contact Information
- Miss AsianComNet
- as******@usssociety.org
Previous Conferences
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2024-10-24 Thailand Bangkok
2024 Asian Conference on Communication and Networks
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