"By 2020, more than 20 billion IoT devices will be in operation, and these devices can leave people vulnerable to security breaches that can put their personal data at risk or worse, affect their safety," said Beulah Samuel, a student in the Penn State World Campus information sciences and technology program. "Yet no strategy exists to identify when and where a network security attack on these devices is taking place and what such an attack even looks like."
The team applied a combination of approaches often used in traditional network security management to an IoT network simulated by the University of New South Wales Canberra. Specifically, they showed how statistical data, machine learning and other data analysis methods could be applied to assure the security of IoT systems across their lifecycle. They then used intrusion detection and a visualization tool, to determine whether or not an attack had already occurred or was in progress within that network.
One of the data analysis techniques the team applied was the open-source freely available R statistical suite, which they used to characterize the IoT systems in use on the Canberra network. In addition, they used machine learning solutions to search for patterns in the data that were not apparent using R.
"One of the challenges in maintaining security for IoT networks is simply identifying all the devices that are operating on the network," said John Haller, a student in the Penn State World Campus information sciences and technology program. "Statistical programs, like R, can characterize and identify the user agents."