DDoS Attack Detection on Internet o Things using Unsupervised Algorithms

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Hailye Tekleselase
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引用次数: 0

Abstract

The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.
基于无监督算法的物联网DDoS攻击检测
物联网网络部署的增加提高了人类和组织的生产力。然而,由于物联网设备固有的安全性较弱和资源受限的特性,物联网网络正日益成为DDoS攻击的平台。本文的重点是通过使用无监督机器学习算法将传输层上的传入网络数据包分类为“可疑”或“良性”来检测物联网网络中的DDoS攻击。在这项工作中,分别训练了两种深度学习算法和两种聚类算法来缓解DDoS攻击。我们重点研究了基于利用的DDOS攻击,包括TCP SYN-Flood攻击和UDP-Lag攻击。在实验阶段,我们使用Mirai、BASHLITE和CICDDoS2019数据集来训练算法。使用准确率评分和归一化互信息评分来量化四种算法的分类性能。我们的结果表明,自动编码器在所有数据集上表现最好,精度最高。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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