Securing Edge-based IoT Networks with Semi-Supervised GANs

With the phenomenal growth of IoT devices and their exponentially increasing applications at the edge of the network, it has become imminent to provide secure, flexible, and programmable network services to support user privacy, security, and quality of service. However, such services can be only be enabled and effectively applied when the edge systems automatically identify these devices. Existing IoT classification systems are based on supervised training methods that require labeled data and manual feature extraction. Such approaches suffer from many challenges such as privacy, labeling efforts, and adaptability to new devices. This paper develops a multistage multi-class classifier based on semi-supervised generative adversarial networks that perform automatic feature extraction with minimal labeled data. The classifier can identify devices with 96% accuracy when only 3% of the training data is labeled. Moreover, the classifier can infer the device type (IoT, Non-IoT, and anomaly) of any new device correctly with 90% accuracy. We also show how our model can support novelty detection, such as zero-day malware attacks.