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2023 IEEE 17th International Symposium on Medical Information and Communication Technology (ISMICT)最新文献

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A Distributed Cybersecurity Solution in an IoMT Network Using a Multi-target Federated Learning 基于多目标联邦学习的IoMT网络分布式网络安全解决方案
Zie Eya Ekolle, Ryuji Kohno, H. Ochiai
Most cybersecurity solutions are based on building a single logical entity to tackle one or multiple cyber threats. Such solutions have proven effective but can be less advantageous in a situation where cyber threats are so many, such that building a detection mechanism will imply writing a lot of logical expressions. Such solutions can be less effective on a specific threat and resource exhaustive on multiple threats. This can be risky to implement in delicate environments such as the medical IoT environment, where an optimal threat detection precision is required. In this paper, we introduce a distributed cybersecurity solution where the logical entities (agents) are distributed in federation along a threat profile, and their individual security operations are merged to produce a globalized security solution over a larger threat profile. We did experiment on this proposed solution and compare the results with those of conventional cybersecurity solution.
大多数网络安全解决方案都是基于构建单个逻辑实体来应对一个或多个网络威胁。这些解决方案已被证明是有效的,但在网络威胁如此之多的情况下可能不那么有利,因为构建检测机制将意味着编写大量逻辑表达式。这类解决方案在处理特定威胁时可能效果较差,而在处理多种威胁时资源耗尽。在需要最佳威胁检测精度的医疗物联网环境等微妙环境中实施这可能存在风险。在本文中,我们介绍了一种分布式网络安全解决方案,其中逻辑实体(代理)沿着威胁配置文件以联邦形式分布,并且将其单独的安全操作合并以在更大的威胁配置文件上生成全球化的安全解决方案。我们对所提出的解决方案进行了实验,并与传统的网络安全解决方案进行了比较。
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2023 IEEE 17th International Symposium on Medical Information and Communication Technology (ISMICT)
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