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An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

机译：改进的基于LDA的ELM分类用于物联网应用的入侵检测算法

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摘要

The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.
机译：物联网（IoT）广泛应用于现代人类生活中，例如智能家居和智能交通。但是，它容易受到恶意攻击，并且当前现有的安全机制无法完全保护IoT。入侵检测作为一种安全技术，可以保护IoT设备免受大多数恶意攻击。然而，不幸的是，传统的入侵检测模型在时间效率和检测效率方面存在缺陷。因此，在本文中，我们为入侵检测算法（ILECA）提出了一种改进的基于线性判别分析（LDA）的极限学习机（ELM）分类。首先，我们改进线性判别分析（LDA），然后使用它来减少特征尺寸。此外，我们使用单个隐藏层神经网络极限学习机（ELM）算法对降维数据进行分类。考虑到物联网设备对检测效率的高要求，我们的方案不仅确保了入侵检测的准确性，而且提高了执行效率，可以快速识别入侵。最后，我们在NSL-KDD数据集上进行实验。评估结果表明，所提出的ILECA具有良好的泛化和实时性，检测精度高达92.35％，优于其他典型算法。

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