Online Anomaly detection for IoT Networks
There is widespread concern that development of IoT devices is performed without sufficient attention paid to security and privacy issues. Consequently, networks have a higher probability of incorporating vulnerable IoT devices that may be easy to compromise to launch cyber-attacks. Inclusion of IoT devices paves the way for a new category of anomalies to be introduced to networks. Here, we investigate the ability to find anomalies in IoT traffic via monitoring network communication patterns and using various fingerprinting and simple machine learning mechanisms.