We studied the security practices of a diverse set of Internet of Things (IoT) devices with different architectures. We found vulnerabilities that can be exploited to launch novel attacks. The real-world implications of IoT attacks show the risks associated with these new technologies, and can help us articulate the need for better security practices.

The Internet of Things (IoT) enables us to sense and share information of real-world events, including potentially privacy-sensitive information about the users' choices and behaviors. In this paper we focus on the security and privacy problems of Internet-connected cameras. We study two cameras: a consumer camera marketed as a baby monitor, and a surveillance camera marketed for enterprise (physical) security. We show how a generic algorithm can be used to infer actions recorded by the camera, even if the traffic is encrypted, and we also show how both cameras have security vulnerabilities that allow a remote attacker to gain access to the video frames captured by the camera. We also discuss new findings such as the fact that one camera has multiple vendors and domains that connect to a single cloud system supported by a single company, which is a trend we have previously seen in other IoT devices with one company designing the core-functionality of the device and then multiple vendors selling the device under their own brand name and developing different mobile applications for them.

This article proposes new tools to detect the tampering of video feeds from surveillance cameras. Our proposal illustrates the unique cyber-physical properties that sensor devices can leverage for their cyber-security. While traditional attestation algorithms exchange digital challenges between devices authenticating each other, our work instead proposes challenges that manifest physically in the field of view of the camera (e.g., a QR code in a display). This physical (challenge) and cyber (verification) attestation mechanism can help protect systems even when the sensors (cameras) and actuators (a display, infrared LEDs, color light bulbs) are compromised. In this article, we consider skillful adversaries that can capture the correct challenges (our system is sending) and can re-create them in the response to try fooling our verification system, and we propose new algorithms to detect these powerful attackers. Also, we introduce new visual challenges that make harder for anti-forensics attackers to succeed, and we present experimental results showing how our system is robust against a variety of attacks ranging from naive attacks to more sophisticated anti-forensics attackers.