In recent years, the Internet has become ubiquitous in our lives and the number of devices connected to the Internet continues to grow at an astounding rate. Much of this growth is due to devices that would traditionally not have access to the Internet, gaining the ability to communicate with other devices through the Internet and thereby creating an "Internet of Things" (IoT).
Since IoT devices are connected through the Internet, they too suffer from the same types of attacks that traditional Internet-connected machines suffer from. However, IoT devices have different properties than traditional machines. These differences prevent many traditional attack detection and prevention methods from being effective in an IoT environment.
In this presentation, we present a framework for the smart home environment that handles the unique properties of IoT networks. This framework uses a two-mode adaptive security model to allow users to balance security and performance. We show the efficacy of our framework in two case studies that address two common categories of attacks in the smart home environment: sinkhole and DDoS attacks. In particular, we take existing intrusion detection and prevention systems (IDS/IPS) for the traditional Internet and transform them to be effective for IoT. We evaluate the effectiveness of our method by comparing it to the traditional systems for both cases in terms of detection accuracy and resource consumption. From the evaluations, we show that our method performs better for IoT environments over the traditional methods.