On the Feasibility of Deep Learning in Sensor Network Intrusion Detection
On the Feasibility of Deep Learning in Sensor Network Intrusion Detection
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DOI：
10.1109/lnet.2019.2901792
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Safa Otoum;B. Kantarci;H. Mouftah
中科院分区：
文献类型：
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作者：
Safa Otoum;B. Kantarci;H. Mouftah
在这封信中，我们提出了一个全面的分析使用机器和深度学习(DL)解决方案的入侵检测系统在无线传感器网络(WSNs)。为了达到这一目的，我们引入了受限Boltzmann机器为基础的集群入侵检测系统(RBC-IDS)，这是一种潜在的基于DL的入侵检测方法，用于监测无线传感器网络的关键基础设施。我们研究了RBC-IDS的性能，并将其与以前提出的基于自适应机器学习的入侵检测系统进行了比较：自适应监督和聚类混合入侵检测系统(Asch-IDS)。数值结果表明，虽然RBC-IDS的检测时间大约是Asch-IDS的两倍，但RBC-IDS和Asch-IDS的检测正确率和检测率是相同的。
In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS.