| dc.description.abstract | In recent years security is an increased concern for IoT devices. Due to limited capabilities compared to traditional computer systems, these tiny devices cannot run the heavy encryption algorithms required for preventing attacks. Nowadays, IoT comprises several communication protocols like Bluetooth Low energy, WiFi, and Zigbee for different applications including home automation, smart city etc. With such a heterogeneous system, it becomes complex to provide security as with every different protocol comes more vulnerabilities in the network. Anomaly-based detection methods have received increasing interest from the scientific community in the last few years. It acts as a second layer to the system’s security. With deep packet inspection, it evaluates the network traffic and forms a set of informative features formalizing the normal and anomalous behaviour of the system. We classify among a normal or abnormal activity using machine learning algorithms and present the results of our detection system implemented on a heterogeneous IoT testbed. This system is applicable for companies, offices, government organization or secret agencies who want to increase their network security to protect their systems. | |
