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首页> 外文期刊>Journal of ambient intelligence and humanized computing >The multi-demeanor fusion based robust intrusion detection system for anomaly and misuse detection in computer networks
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The multi-demeanor fusion based robust intrusion detection system for anomaly and misuse detection in computer networks

机译：基于多风度融合的计算机网络中的异常和滥用检测的多风度融合

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摘要

Combating the cyber threats, particularly attack detection, is a challenging area of the intrusion detection system (IDS). Exceptional development and internet usage raise concerns about how digital data can be securely communicated and protected. This work has proposed a multi demeanor fusion-based intrusion detection system where stream data mining based on ST-SR (stochastic relaxation). Thus it includes uncertain c capitals clustering with multi data fusion is incorporated to classify the fused network traffic information effectively. Subsequently, classified data would be sent to the web usage mining based on a stochastic Latent Semantic and synthetic Analyzer which analyzes the traffic information. Even though being classified and analyzed the network traffic information itself can't get connected to the secured network due to its dynamic nature so to handle this situation, this work has incorporated IDS model (intrusion detection model based on parallel ensemble using bagging) which predicts the quality of service of each network during network traffic and enables the user to get connected with a secured network which holds high packet delivery ratio, less packet loss, and high throughput.
机译：打击网络威胁，特别是攻击检测，是入侵检测系统（IDS）的具有挑战性区域。特殊的开发和互联网使用提出了关于如何安全地传达和保护数字数据的担忧。这项工作提出了一种基于多举行的融合性入侵检测系统，基于ST-SR（随机弛豫）的流数据挖掘。因此，它包括具有多数据融合的不确定C的Capitals集群被融合，以有效地对融合的网络流量信息进行分类。随后，基于随机潜在语义和合成分析器的网络使用挖掘将分类数据发送到网络使用挖掘，该分析数据分析了交通信息。即使被分类和分析到网络流量信息本身由于其动态性质而无法接触到安全网络，以便处理这种情况，因此该作品已采纳IDS模型（使用袋装的基于并行集合的入侵检测模型）预测在网络流量期间每个网络的服务质量，使用户能够与拥有高包传递比，较少丢包和高吞吐量的安全网络连接。

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