报告详情
Intelligent Android Malware Detection in Real-World Environments Using Machine and Deep Learning Model
编号：149
访问权限：仅限参会人
更新：2025-12-23 13:19:08 浏览：110次
拓展类型1
报告开始：2025年12月29日 14:15（Asia/Amman）
报告时间：15min
所在会场：[S3] Track 3: Privacy, Security for Networks [S3] Track 3: Privacy, Security for Networks
摘要
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.
关键词
Android malware, machine learning, malware detection, Mobile security, deep learning, TUANDROMD dataset, cybersecurity
报告人
Adnan Al-Smadi
Professor Zarqa University稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
-
02月10日 2026
初稿截稿日期
-
02月10日 2026
注册截止日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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