Creating a LoRa network dataset with normal and attack traffic

The increasing prevalence of the Internet of Things (IoT) in our society and emphasizes the necessity of safeguarding the cybersecurity of IoT devices. Intrusion Detection Systems (IDS) are a viable solution to protect IoT devices from cyberattacks. To evaluate and enhance the efficacy of such systems, access to comprehensive datasets of malicious traffic is crucial. This thesis introduces a methodology to capture and simulate attacks on LoRaWAN networks to generate these datasets. In addition, an analysis of the resultant network datasets was conducted, enabling the identification of the impact of attacks on LoRaWAN traffic compared to normal traffic behaviour. The report also offers insights into potential areas of focus for attack detection. Overall, this study aims to offer valuable resources for researchers engaged in developing tools like Intrusion Detection Systems for LoRaWAN networks.