Infantry
发表于 2025-3-28 16:14:21
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索赔
发表于 2025-3-28 20:08:40
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加入
发表于 2025-3-29 01:28:35
Automated Identification of Sensitive Data via Flexible User Requirementsch is able to learn users’ preferences from readable concepts initially provided by users, and automatically identify related sensitive data. We evaluate our approach on over 18,000 top popular applications from Google Play Store. S3 achieves an average precision of 89.2%, and average recall 95.8% i
DECRY
发表于 2025-3-29 03:46:08
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制定
发表于 2025-3-29 11:18:32
Inferring UI States of Mobile Applications Through Power Side Channel Exploitationetection, PoWatt can still detect sensitive UIs with a reasonable precision and recall, which can be successfully exploited by real-world attacks such as screenshot-based password stealing. Finally, we discuss the limitations of PoWatt and possible mitigation techniques.
Rotator-Cuff
发表于 2025-3-29 14:56:12
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prosperity
发表于 2025-3-29 15:43:32
GranDroid: Graph-Based Detection of Malicious Network Behaviors in Android Applications accuracy. Our evaluation using 1,500 malware samples and 1,500 benign apps shows that our approach achieves 93% accuracy while spending only eight minutes to dynamically execute each app and determine its maliciousness. . can be used to provide rich and precise detection results while incurring sim
热心
发表于 2025-3-29 23:00:43
FGFDect: A Fine-Grained Features Classification Model for Android Malware Detectiono benign or malware. FGFDect is evaluated on a large real-world data set consisting of 6400 malware apps and 4600 popular benign apps. Compared with those traditional approaches with coarse-grained features, extensive evaluation results demonstrate that the proposed approach exhibits an impressive d
Minutes
发表于 2025-3-30 03:57:43
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engihttp://image.papertrans.cn/s/image/863500.jpg
赌博
发表于 2025-3-30 07:36:14
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