Implementation of Blockchain with Machine Learning Intrusion Detection System for Defending IoT Botnet and Cloud Networks

Abstract

Significant research has been done on combining intrusion detection and blockchain to increase data privacy and find both current and future threats. This research suggests a machine blockchain framework (MBF) in order to provide distributed intrusion detection with security and blockchain with privacy with the help of smart contracts in IoT networks. An XGBoost algorithm was implemented to work with sequential network data and the intrusion detection approach is explored using the N-BaIoT dataset. In order to protect the network against known malware threats (Mirai, Gafgyt, or Bashlite), the IoT malware attack prediction model created in this study offers a deterrent strategy based on the network traffic statistics. On the other hand, the models need to be taught to recognize new varieties of malware. In this work, we observe how different machine learning models, like Random Forest algorithm and proposed XGBoost algorithm, can accurately predict the infected malware in certain traffic instance. However, we provide a honeypot-based strategy that employs machine learning techniques for the detection of malware in this study. Using data from an IoT Botnet as a dataset helps train a machine learning model in a way that is effective and changes over time.