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Titlebook: Machine Learning for Cyber Security; Second International Xiaofeng Chen,Xinyi Huang,Jun Zhang Conference proceedings 2019 Springer Nature S

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31#
發(fā)表于 2025-3-26 23:41:02 | 只看該作者
32#
發(fā)表于 2025-3-27 02:33:12 | 只看該作者
Secure Multiparty Learning from Aggregation of Locally Trained Models,o the local private datasets. The generalization performance of . is excellent and almost equals to the accuracy of the model learned from the union of all the parties’ datasets. We implement . on MNIST, and extensive analysis shows that our method is effective, efficient and secure.
33#
發(fā)表于 2025-3-27 07:06:03 | 只看該作者
34#
發(fā)表于 2025-3-27 10:07:26 | 只看該作者
35#
發(fā)表于 2025-3-27 17:23:29 | 只看該作者
Quantifiable Network Security Measurement: A Study Based on an Index System,We illustrate the corresponding theories and the usages of each selected indicators and we also complete the real-time security measurement in various attacks and defenses by using NS3 simulator. The simulation results verify the correctness and rationality of the proposed Security Measurement Index System.
36#
發(fā)表于 2025-3-27 21:49:05 | 只看該作者
37#
發(fā)表于 2025-3-27 22:45:23 | 只看該作者
An Enumeration-Like Vector Sampling Method for Solving Approximate SVP,new sampling method is a universal framework that can be embedded into most of the sampling-reduction algorithms. The experimental result shows that sampling reduction algorithm with the new sampling method embedded runs faster than the original Restricted Reduction (RR) algorithm within 90 dimensions.
38#
發(fā)表于 2025-3-28 04:31:08 | 只看該作者
39#
發(fā)表于 2025-3-28 08:56:51 | 只看該作者
A Lightweight Secure IoT Surveillance Framework Based on DCT-DFRT Algorithms,d on discrete fractional random transform (DFRT) and Chen chaotic system. The proposed framework is fast and ensures real-time processing. Furthermore, this framework has the ability to reduce the transmission cost, and storage required during transmitting the video surveillance.
40#
發(fā)表于 2025-3-28 10:52:09 | 只看該作者
0302-9743 apers detail all aspects of machine learning in network infrastructure security, in network security detections and in application software security..978-3-030-30618-2978-3-030-30619-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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