Threat Hunting in Internet of Things Networks with Bio-Inspired Models

Cyber threat hunting is time-consuming as large data quantities are analysed to hunt down attacks that have evaded existing security measures. With an increasing number of Internet of Things (IoT) devices, the subsequently increasing data quantities make finding attacks through traditional approaches impossible. Existing research has highlighted the use of signature-based, visualisation, analytical, and Artificial Intelligence (AI) approaches to threat detection and anomaly detection. This paper contributes to the research on threat hunting using AI and presents novel models for threat hunting in IoT networks. The proposed models effectively demonstrate the importance of preprocessing approaches and model adaptability while challenging the need for overly complicated models.