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Titlebook: Computational Intelligence in Security for Information Systems 2010; Proceedings of the 3 álvaro Herrero,Emilio Corchado,ángel Alonso Confe

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樓主: trace-mineral
31#
發(fā)表于 2025-3-27 00:50:59 | 只看該作者
Arndt Kaminski Ph.D,Carsten Rahlf,Inga Vossnown and labeled data in order to assist a clustering process. We propose two different fingerprinting approaches. The first one is using behavioral features that are induced from a protocol state machine. The second one is relying on the underlying parse trees of messages. Both approaches are passi
32#
發(fā)表于 2025-3-27 04:08:33 | 只看該作者
Strategische Führung auf dem Prüfstandlecting a subset of flow related information that summarizes communication endpoints, volume, status and time parameters. Commonly known as NetFlow records, the recent development of a standardized protocol and data format, as well as the support from all major vendors, did make the processing, coll
33#
發(fā)表于 2025-3-27 05:25:40 | 只看該作者
34#
發(fā)表于 2025-3-27 12:34:06 | 只看該作者
https://doi.org/10.1007/978-3-642-05474-7eral security issues. Despite privacy concerns begin to emerge, there are still other dangerous vulnerabilities that affect security and threaten organisations and users assets. In this paper, we present the first Threat Modelling approach in Online Social Networks that intends to identify the threa
35#
發(fā)表于 2025-3-27 14:53:05 | 只看該作者
álvaro Herrero,Emilio Corchado,ángel AlonsoRecent research in Computational Intelligence in Security for Information Systems.Contains accepted papers presented at CISIS’10, which was held in León, Spain, on November 11th-12th, 2010.Written by
36#
發(fā)表于 2025-3-27 19:11:48 | 只看該作者
37#
發(fā)表于 2025-3-27 22:29:36 | 只看該作者
Computational Intelligence in Security for Information Systems 2010978-3-642-16626-6Series ISSN 1867-5662 Series E-ISSN 1867-5670
38#
發(fā)表于 2025-3-28 05:43:24 | 只看該作者
39#
發(fā)表于 2025-3-28 07:45:53 | 只看該作者
40#
發(fā)表于 2025-3-28 10:40:51 | 只看該作者
https://doi.org/10.1007/978-3-663-08992-6in’) hypothesis, describes successfully ROCs of any form, supports semi-representational memory architectures and predicts a kind of an irremovable physiology-behavior or brain-mind uncertainty. Results provide a ground for designing biologically inspired intelligent high performance codes and devices.
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