An Intrusion Detection System Against DDoS Attacks in IoT Networks

Monika Roopak, G. Tian, J. Chambers
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引用次数: 59

Abstract

In this paper, we present an Intrusion Detection System (IDS) using the hybridization of the deep learning technique and the multi-objective optimization method for the detection of Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT) networks is proposed in this paper. IoT networks consist of different devices with unique hardware and software configurations communicating over different communication protocols, which produce huge multidimensional data that make IoT networks susceptible to cyber-attacks. In a network the IDS is a vital tool for securing it from cyber-attacks. Detection of new emerging cyber threats are becoming difficult for existing IDS, and therefore advanced IDS is required. A DDoS attack is a cyber-attack that has posed substantial devastating losses in IoT networks recently. In this paper, we propose an IDS founded on the fusion of a Jumping Gene adapted NSGA-II multi-objective optimization method for data dimension reduction and the Convolutional Neural Network (CNN) integrating Long Short-Term Memory (LSTM) deep learning techniques for classifying the attack. The experimentation is conducted using a High-Performance Computer (HPC) on the latest CISIDS2017 datasets on DDoS attacks and achieved an accuracy of 99.03% with a 5-fold reduction in training time. We evaluated our proposed method by comparing it with other state-of-the-art algorithms and machine learning algorithms, which confirms that the proposed method surpasses other approaches.
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针对物联网网络DDoS攻击的入侵检测系统
本文提出了一种融合深度学习技术和多目标优化方法的入侵检测系统(IDS),用于检测物联网(IoT)网络中的分布式拒绝服务(DDoS)攻击。物联网网络由不同的设备组成,这些设备具有独特的硬件和软件配置,通过不同的通信协议进行通信,从而产生巨大的多维数据,使物联网网络容易受到网络攻击。在网络中,IDS是保护网络免受网络攻击的重要工具。现有的入侵检测系统越来越难以检测新出现的网络威胁,因此需要先进的入侵检测系统。DDoS攻击是一种网络攻击,最近在物联网网络中造成了巨大的破坏性损失。在本文中,我们提出了一种基于跳跃基因的NSGA-II多目标优化方法的IDS,该方法用于数据降维和卷积神经网络(CNN)集成长短期记忆(LSTM)深度学习技术的攻击分类。实验使用高性能计算机(HPC)在最新的CISIDS2017 DDoS攻击数据集上进行,准确率达到99.03%,训练时间减少了5倍。我们通过将我们提出的方法与其他最先进的算法和机器学习算法进行比较来评估我们提出的方法,这证实了我们提出的方法优于其他方法。
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