The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

Distributed Denial-of-Service (DDoS) attacks are increasing in frequency and volume on the Internet, and there is evidence that cyber-criminals are turning to Internet-of-Things (IoT) devices such as cameras and vending machines as easy launchpads for large-scale attacks. This paper quantifies the capability of consumer IoT devices to participate in reflective DDoS attacks. We first show that household devices can be exposed to Internet reflection even if they are secured behind home gateways. We then evaluate eight household devices available on the market today, including lightbulbs, webcams, and printers, and experimentally profile their reflective capability, amplification factor, duration, and intensity rate for TCP, SNMP, and SSDP based attacks. Lastly, we demonstrate reflection attacks in a real-world setting involving three IoT-equipped smart-homes, emphasising the imminent need to address this problem before it becomes widespread.

Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of networks ranging from the data center to the core, enterprise, and home. The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.