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As it is with anything that is connected to the Internet, security is one of the paramount concerns of IoT. While not that big a deal for smartphones and desktops, IoTs are left more vulnerable than traditional devices when it comes to security exploits. If you’re wondering why, then the reason lies in the very way that IoT devices are constructed.
Internet Security: A Very Real Threat to IoT Devices
Built with a very low power consumption and low clock rate processor, IoT devices often have very limited storage and processing capabilities in order to keep their footprint, cost as well as power consumption in check. However, this one great strength of IoT devices that enable such machines to be deployed in the thousands can lead to catastrophic security breaches as well.
Once detected, a network of such IoT connected devices can be very easy to hack into for a malicious person and once a device is compromised, valuable information can be stolen from them in real time. Added to that, there is the possibility that the IoT devices which are connected to a network will form the gateway into further attacks into the network itself thus endangering the core compute unit as well as other devices attached to the network.
Using Machine Learning As A Security Tool
So how can we protect IoT devices by using machine learning? Well as it turns out the limited nature of IoT devices which make them such an excellent target for hackers, also makes them an excellent target for Machine Learning. While Neural Networking and Machine Learning is already being used extensively by researchers to analyze the data collected by IoT devices and reduce the cost, increase efficiency; they can also be used for security purposes.
As it is with most IoT machines, like smart bulbs and hubs, the narrow scope of use leads to very predictable usage patterns. In fact, for most connected devices we can see a pattern forming among users across the world. Now this makes it much easier for machine learning networks as they can effectively single out devices that are behaving anomalously and then proceed to restrict access to them.
However, there is a caveat in here. As IoT is a fledgling industry, there is not much data that has been collected on the behavior of devices as of yet. As a result, the chances of a Machine Learning AI to encounter false positives is at an all time high. This is why at this stage of IoT security, the machine learning must be augmented by a human analyst who can perceive if it is an attack happening in real time and then provide feedback on that back to the AI.
“Machine learning is a critical component to developing Artificial Intelligence for IoT security.”, says Uday Veeramachaneni, co-founder, and CEO at PatternEx.
With IoT reaching massive scales of deployment in the coming years, security of data and of the devices themselves become on of the primary concerns for researchers and developers going forward. And the amalgamation of AI and Human brains in an augmented Machine Learning model seems to be our best bet to solving this pressing issue.