  The Internet of Things(IoT) paradigm provides persistent sensing and data
collection capabilities and is becoming increasingly prevalent across many
market sectors. However, most IoT devices emphasize usability and function over
security, making them very vulnerable to malicious exploits. This concern is
evidenced by the increased use of compromised IoT devices in large scale bot
networks (botnets) to launch distributed denial of service(DDoS) attacks
against high value targets. Unsecured IoT systems can also provide entry points
to private networks, allowing adversaries relatively easy access to valuable
resources and services. Indeed, these evolving IoT threat vectors (ranging from
brute force attacks to remote code execution exploits) are posing key
challenges. Moreover, many traditional security mechanisms are not amenable for
deployment on smaller resource-constrained IoT platforms. As a result,
researchers have been developing a range of methods for IoT security, with many
strategies using advanced machine learning(ML) techniques. Along these lines,
this paper presents a novel generative adversarial network(GAN) solution to
detect threats from malicious IoT devices both inside and outside a network.
This model is trained using both benign IoT traffic and global darknet data and
further evaluated in a testbed with real IoT devices and malware threats.
