Fabric
发表于 2025-3-26 21:52:35
Nutzeinkommensmasse (Samuelson, Hicks)on, preprocessing, feature extraction, learning algorithms, and evaluation. Finally, we highlight some challenges and open issues in data mining-based android malware detection. This review will help understand the complete picture of static android malware detection and serve as a basis for malware detection in general.
jeopardize
发表于 2025-3-27 03:46:29
Improving the Efficiency of WebRTC Layered Simulcast Using Software Defined Networkingbridge and tested in the GEANT testbed network. The results showed that our solution significantly reduces problems related to available throughput overshooting, which is typical for layered simulcast.
cortex
发表于 2025-3-27 08:23:13
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构想
发表于 2025-3-27 10:51:21
Conference proceedings 2023ystems, Broadnets 2022, which took place in March 2023. Due to COVID-19 pandemic the conference was held virtually.. The 9 full papers presented were carefully reviewed and selected from 23 submissions. The papers are thematically grouped as follows: Mobile Communication Networks; Mobile Software Se
STALE
发表于 2025-3-27 14:18:34
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朦胧
发表于 2025-3-27 20:37:55
A New Approach for Measuring Delay in 5G Cellular Networkss of metrics such as delay or reliability. Accordingly, operators need new tools to measure these metrics in different realistic, often extreme, conditions, so that they can evaluate the degree of fulfilment of their service levels. In this work we propose a simple practical framework to evaluate th
stroke
发表于 2025-3-27 23:40:12
Improving the Efficiency of WebRTC Layered Simulcast Using Software Defined Networkinger to improve WebRTC video streaming. The proposed bridge divides the functionality of a classic Selective Forwarding Unit into two parts. The selection of layers is performed by the SDN controller and the forwarding of layered video is still accomplished by the bridge. The bridge and the SDN contro
性满足
发表于 2025-3-28 05:14:00
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收养
发表于 2025-3-28 07:39:25
Android Malware Detection Based on Static Analysis and Data Mining Techniques: A Systematic Literatu for android malware detection. Literature suggests that static malware detection techniques are practical and assuring for detecting android malware. This paper presents a thorough survey of data mining-based static malware detection. We briefly discuss the growth of android malware and current det
冲突
发表于 2025-3-28 12:08:47
MalEfficient10%: A Novel Feature Reduction Approach for Android Malware Detectioncious applications in the ecosystem. Many recent reports suggest that the conventional signature-based malware detection technique fails to protect android smartphones from new and sophisticated malware attacks. Therefore, researchers are exploring machine learning-based malware detection systems th