Data-driven intrusion detection for ambient intelligence

Abstract

Billions of embedded processors are being attached to everyday objects and houseware equipment to enhance daily activities and enable smart living. These embedded processors have enough processing capabilities to process sensor data to produce smart insights, and are designed to operate for months without the need of physical interventions. Despite the compelling features of Internet of Things (IoT), applied at several home-oriented use cases (e.g., lighting, security, heating, comfort), due to the lack of a physical flow of information (e.g., absence of switches and cable-based gateways), the security of such networks is impeding their rapid deployment. In this work we look into IPv6 based IoT deployments, since it is the leading standard for interconnecting the wireless devices with the Internet and we propose a data-driven anomaly detection system that operates at the transport-layer of 6LoWPAN deployments. We present a comprehensive experimental evaluation carried out using both simulated and real-world experimentation facilities that demonstrates the accuracy of our system against well-known network attacks against 6LoWPAN networks.