Smart Attack Detection for IoT Networks

Abstract: The Internet of Things (IoT) is becoming related to more and more people's daily life. It is a network that consists of resource-constrained devices. Nowadays, the application of IoT like smart wearable devices is very common. Due to the wide and important application of IoT, its security also attracts research attention without any doubt. IoT networks are exposed to various attacks, so detecting attacks is necessary to enhance IoT security, which is achieved by Intrusion Detection Systems (IDS). To build an IDS, machine learning can be used as an efficient tool to train intrusion detection models. However, machine-learning methods often consume a lot of memory and computation resources, which inspires research on implementing machine-learning-based IDS on resource-constrained devices for IoT networks. This thesis aims to design and implement a machine-learning-based IDS for IoT networks. The target IoT devices are installed with an embedded operating system called Contiki. A large dataset of IoT networks is first created, which covers numerous network topologies. Then an intrusion detection classifier is trained using the Random Forests algorithm. The IDS is implemented by integrating the trained classifier with devices with the Contiki system. We perform experiments both in simulation and on real devices to evaluate the proposed IDS. The results show that our IDS works well on Contiki nodes in IoT networks. In experiments based on simulation, the detection accuracy always achieves over 92% under different setups. In the experiments on real resource-constrained devices, the IDS gets a detection accuracy of 100% in 15 different network topologies.