DDoS AttaDetection based on Machine Learning Algorithms in IoT Environments

• Mehdi Ebady Manaa ^[2] ; Saba M. Hussain ^[1] ; Suad A. Alasadi ^[1] ; Hussein A. A. Al-Khamees ^[3]
  1. [1] University of Babylon

     University of Babylon

     Irak

     
  2. [2] Al-Mustaqbal University- College of Science
  3. [3] Al-Mustaqbal University

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• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 27, Nº. 74, 2024, págs. 152-165
• Idioma: inglés
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  • In today’s digital era, most electrical gadgets have become smart, and the great majority of them can connect to the internet. The Internet of Things (IoT) refers to a network comprised of interconnected items. Cloud-based IoT infrastructures are vulnerable to Distributed Denial of Service (DDoS) attacks. Despite the fact that these devices may be accessed from anywhere, they are vulnerable to assault and compromise. DDoS attacks pose a significant threat to network security and operational integrity. DDoS assault in which infected botnets of networks hit the victim’s PC from several systems across the internet, is one of the most popular. In this paper, three prominent datasets: UNSW-NB 15, UNSW-2018 IoT Botnet and recent Edge IIoT are using in an Anomaly-based Intrusion Detection system(AIDS) to detect and mitigate DDoS attacks. AIDS employ machine learning methods and Deep Learning (DL) for attack mitigation. The suggested work employed different types of machine learning and Deep Learning (DL): Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, and Multi-layer perceptron (MLP), deep Artificial Neural Network (ANN), and Long Term Short Memory (LSTM) methods to identify DDoS attacks. Both of these methods are contrasted by the fact that the database stores the trained signatures. As a results, RF shows a promising performance with 100% accuracy and a minimum false positive on testing both datasets UNSW-NB 15 and UNSW-2018 Botnet. In addition, the results for a realistic Edge IIoT dataset show a good performance in accuracy for RF 98.79% and for deep learning LSTM with 99.36% in minimum time compared with other results for multi-class detection.