基于异常的移动自组织网络入侵检测系统:一个实用的理解

S. Valiveti, Anush Manglani, Tadrush Desai
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引用次数: 3

摘要

自组织网络用于异构环境,如战术军事应用,其中没有中央协调的基础设施可用。要求网络具备自配置、动态拓扑管理功能,保证网络的自持续性。因此,安全是最重要的。基于异常的入侵检测系统(IDS)是一种分布式的活动,由网络的所有节点协同路由等相关网络活动共同完成。机器学习及其进步已经在异常检测中找到了一个有前途的地方。本文描述了为战术应用程序实现IDS定义最合适的路由协议的过程,以及相关合适数据集的选择。本文还回顾了最新的机器学习技术、实现能力和局限性。
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Anomaly-Based Intrusion Detection Systems for Mobile Ad Hoc Networks: A Practical Comprehension
Ad hoc networks are used in heterogeneous environments like tactical military applications, where no centrally coordinated infrastructure is available. The network is required to perform self-configuration, dynamic topology management, and ensure the self-sustainability of the network. Security is hence of paramount importance. Anomaly-based intrusion detection system (IDS) is a distributed activity carried out by all nodes of the network in a cooperative manner along with other related network activities like routing, etc. Machine learning and its advances have found a promising place in anomaly detection. This paper describes the journey of defining the most suitable routing protocol for implementing IDS for tactical applications, along with the selection of the related suitable data set. The paper also reviews the latest machine learning techniques, implementation capabilities, and limitations.
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