Internet of Things was a not so popular term just a few years ago. Many people did not hear about it. A lot many didn’t even know that it was soon going to be part of their everyday lives in the form of devices. Today IoT devices are a normal feature. Wearable devices like Fitbit are not just health indicators but also fashion quotients.

IoT devices are everywhere and in every sphere – homes, agricultural fields, automobile sector, health care and medical fields, education, the list is really endless. It is estimated that close to 50 billion IoT devices will be in use by 2020. That’s a huge number of devices!

IoT devices unlike computers are vulnerable to cyberattacks. They are smart but not smart enough to ward off security threats. Internet of things security is a growing concern with deepened use of IoT devices. Thankfully, machine learning can make internet of things security a reality.

How Does Machine Learning Make IoT Devices Safe?

Machine learning algorithms help computers find hidden insights in seemingly normal transactions. These algorithms are based on advanced data analysis and analytical model building. Machine learning and IoT security is a combination that will make using IoT devices safer.

IoT devices generate lot of data. Machine learning is applied to this data to gather insights. These insights are generally around improving customer satisfaction or reducing cost. This equation can be flipped to understand the aspects of this data that are harmful. The mechanics remain same but the focus needs to shift from customer centric analytics to security centric analytics.

Cloud as a Means of Aiding Machine Learning for IoT Security

An IoT device needs a security key to function. This is good news. Imagine a situation where you have a smart home with all smart appliances powered by IoT. Further imagine having a security key for each of these smart appliances. It is no longer good news. Instead it is a nightmare. A possible solution can be to have a unified security key. But, that leaves ample room for all kinds of hacking and security issues.

Let’s look at this situation in a different light. You have a smart home with all these smart IoT powered devices. Thankfully, because these are devices, they have unique set of functions and are predictable in outcomes. So you have a huge but still countable set of unique functions or formats of use that a device can be put to. If you find even a single anomaly in the way these functions happen, then you know there is a potential issue. So if you can house all these finite unique set of IoT device functions on a cloud and use machine learning to identify potential issues, you have a solution in hand. This is exactly how cloud computing and machine learning are used to build a IoT security environment.

All IoT devices need a stable internet connection to function. Anything connected to the net generates reports that reside on the network. Cloud computing can be a possible residence for all these reports generated by IoT devices. Machine learning algorithms can be used to identify potential issues on this centralized repository of diagnostic reports.

Routing machine learning for cyber security via a cloud works in the favor of IoT security as the amount of data available makes it easier to run the algorithms. Further, a solution patch can be reissued at a faster rate, making the use of IoT devices secure. Using the cloud as a means to host machine learning algorithms to fight IoT security issues is widely used across various organizations.

Machine Learning and Human Intervention

IoT devices are widespread. If there is a cyberattack, then the resultant chaos is wide spread too. Any solution for IoT security needs to be oriented to handle such large volume of devices in real time. Machine learning does a wonderful job of analyzing and identifying trends and threats. However, it has its limitations by the data that is used to design the algorithms.

Machine learning algorithms work based on the patterns that the IoT devices should follow, as input. A slight deviation may or may not be detected as a potential threat. So it might create a sense of false security too.

Clubbing machine learning along with human insight is a solution that will go a long way in IoT security. Machines can be used to dig, store, analyze and understand data. This data can be created as reports that are severely scrutinized by a trained analyst. What a human can detect as a pattern difference might not be understood by a machine and hence the need for this last measure defense in the battle for IoT security.

A combination of machine learning powered by cloud-based data solutions and human intervention will pave the way for greater IoT security.

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