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首页> 外文期刊>Knowledge-Based Systems >ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features
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ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features

机译：ANID-SEoKELM：基于具有随机特征的内核ELM的选择性集合的自适应网络入侵检测

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摘要

This paper presents an adaptive network intrusion detection (ANID) method based on the selective ensemble of kernel extreme learning machines (KELMs) with random features (termed ANID-SEoKELM), aiming at identifying various unauthorized uses, misuses and abuses of computer systems in real time. To generate a lightweight intrusion detector, multiple KELMs are learned independently based on the Bagging strategy with sparse random feature representation (SRFR), to reduce noise and redundant or irrelevant information in network connection instances and ensure the diversity of base learners for the effective ensemble of base learners. A marginal distance minimization (MDM)-based selective ensemble (MDMbSE) method is introduced to generate the ultimate intrusion detector. To ensure the adaptability of the intrusion detector, an incremental learning-based detection-model updating procedure is also derived. Extensive validation and comparative experiments on the benchmark KDD99 dataset and a hybrid heterogeneous network simulation platform mixed with wireless networks and Ethernet networks demonstrate that the ANID-SEoKELM is able to adapt to the dynamically changing network environments hence it can achieve higher detection accuracies stably and efficiently than classic single learner-based intrusion detection methods and representative ensemble-based intrusion detection methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文提出了一种基于具有随机特征的内核极限学习机（KELM）的选择性集合（称为ANID-SEoKELM）的自适应网络入侵检测（ANID）方法，旨在识别实际的各种未经授权的使用，滥用和滥用计算机系统时间。为了生成轻量级的入侵检测器，将基于具有稀疏随机特征表示（SRFR）的Bagging策略独立学习多个KELM，以减少网络连接实例中的噪声和冗余或不相关的信息，并确保基础学习者的多样性，以实现有效的集成。基础学习者。引入基于边际距离最小化（MDM）的选择性集成（MDMbSE）方法来生成最终入侵检测器。为了确保入侵检测器的适应性，还导出了基于增量学习的检测模型更新过程。在基准KDD99数据集以及混合了无线网络和以太网网络的混合异构网络模拟平台上进行的大量验证和比较实验表明，ANID-SEoKELM能够适应动态变化的网络环境，因此可以稳定，高效地实现更高的检测精度。比经典的基于单个学习者的入侵检测方法和基于代表集成的入侵检测方法要强。 （C）2019 Elsevier B.V.保留所有权利。

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