Constructing IoT Botnet Detection Model Based on Degree Centrality and Path Analysis

Internet of Things (IoT) Botnet is a network of connected devices, generally smart devices with software and intelligent sensors, networked over the internet to send and receive data from other intelligent devices infected with IoT Botnet malware. It is very challenging to detect IoT Botnet activity since the targeted devices are IoT devices. IoT Botnet attack patterns have not yet been disclosed. Current IoT Botnet detection is still unable to identify attack patterns, and failing to recognise key IoT Botnet behaviours has led to a loss of ability to meet detection criteria. The purpose of this research study is to identify IoT Botnet behaviour, propose an IoT Botnet attack pattern based on its behaviour, build an IoT Botnet detection model, and validate the selection of the IoT Botnet detection model using the IoT Botnet attack criteria. In addition, an IoT Botnet attack pattern is being developed by combining the IoT Botnet life cycle and IoT Botnet behaviour via IoT Botnet activities. A graph analytics-based IoT Botnet detection model has been created in order to detect IoT Botnet attack activities. The earlier detection of IoT Botnet has been visualised by IoT Botnet attack patterns using degree centrality and path analysis. The outcome demonstrated that the proposed IoT Botnets model met the detection criteria.