While smart devices continue to increase in popularity and use at home and at work, thanks to their convenience, IoT vulnerabilities are being exploited to access and compromise individual devices and the entire networks they’re connected to. With hackers weaponizing consumer, industrial and enterprise IoT devices, it has become an immense challenge to secure every device and every network.
The increasing volume, complexity and sophistication of attacks has caused a shift toward detection models, but there are still significant obstacles when it comes to applying behavioral analysis to monitor network flows and identify malicious behavior in IoT systems. To identify the interactions between systems and understand the traffic flowing between devices inside and outside of a network, there has to be visibility of that particular network. In addition, the volume of network traffic generated by IoT devices has to be processed and classified accordingly. Given the lack of standardization in the field, it’s difficult to build a single model of the normal behavior expected of IoT devices. There’s also a fine line between the ability to detect all malicious events and the probability of missing anomalies in the automated processes, considering there are millions of different IoT devices with different behavior profiles.
If IoT devices are inherently insecure, how can their behavior be used to build a methodology that identifies anomalies, enforces rules to secure the networks that devices are connected to and protects their ecosystems?