Securing the Internet of Things via Machine Learning

By CIOReview | Monday, March 26, 2018
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Machine learning has experienced a remarkable rise in popularity across a very broad range of applications. In today’s IoT network security landscape, one of the major challenges that organizations face is in categorizing the type of data to look for anomalies and determining the significant deviations in the data across the network.

In the present day, there is a significant lack of data that is labeled in cyberspace ecosystem from which the machine learning applications can learn. As a result, detecting anomalies and monitoring cybersecurity become tedious. This presents a great challenge for enterprises and businesses to safeguard the data on an IoT device–that might be running on any of the various wireless protocols.

Machine-learning tools provide great accuracy in classifying image categories by eliminating human error-rate. As these tools analyze the vast amount of data, it gets better at identifying categories over time. With this ability, machine learning can be extended to the cyberspace for classifying data threats.

Most systems that are dependent on machine learning and behavior analysis will garner data about the network and connected devices followed by learning everything that is abnormal. The issue with this primitive approach is that it generates too many counterfeit alarms and false positives. To overcome this challenge, a business must develop a solution that integrates machine learning and verify it with a human analyst for greater accuracy.

 

Machine learning has experienced a remarkable rise in popularity across a very broad range of applications. In today’s IoT network security landscape, one of the major challenges that organizations face is in categorizing the type of data to look for anomalies and determining the significant deviations in the data across the network.

In the present day, there is a significant lack of data that is labeled in cyberspace ecosystem from which the machine learning applications can learn. As a result, detecting anomalies and monitoring cybersecurity become tedious. This presents a great challenge for enterprises and businesses to safeguard the data on an IoT device–that might be running on any of the various wireless protocols.

Machine-learning tools provide great accuracy in classifying image categories by eliminating human error-rate. As these tools analyze the vast amount of data, it gets better at identifying categories over time. With this ability, machine learning can be extended to the cyberspace for classifying data threats.

Most systems that are dependent on machine learning and behavior analysis will garner data about the network and connected devices followed by learning everything that is abnormal. The issue with this primitive approach is that it generates too many counterfeit alarms and false positives. To overcome this challenge, a business must develop a solution that integrates machine learning and verify it with a human analyst for greater accuracy.