Machine learning in IoT Security


Everything, from smart home devices to the monitoring of a production site and the recording of energy usage data, is possible through connected devices. Even businesses have started to get ai consulting to make their workplace much more efficient. IoT includes everything from residential products to mobile devices connected to a network locally or via the internet. But this massive expansion increases the burden on security professionals to keep networks and resources secure.

Machine learning does represent a help in IoT security. “Machine learning and artificial intelligence can help Cybersecurity to take on this challenge, especially as the problem is made more complex due to many IoT vendors considering security little more than an afterthought. The security industry needs to get ahead of the problem were creating and the only way to do that is with Automation”. according to Hal Lonas, CTO of Webroot. Machine learning is not a silver bullet, but it is an essential tool in the box for keeping ahead of, or at least quickly detecting some of the latest types of attacks, Lonas added.

Machine learning is nothing new. It expanded to include deep learning, data analytics, pattern detection and security anomaly alerts. Analyzing and monitoring devices connected to the network are time-consuming and require many tools to watch over all network entry points, which could be exploited. Machine learning can provide the bridge to utilize the massive amounts of data that IoT devices produce, while compiling this information into a pattern that can be used to understand the devices status. If suspicious activities are detected, the operators will give an alert. “Machine learning can monitor thousands of variables, versus the handful that people can, all while never getting tired, lazy or taking a day off”, Lonas said. This in turn gives analysts time to work on more valuable areas of security, reducing the time between threats being introduced and our ability to defend it, he added.

Machine learning can also limit human error and reduce costs, but still within early development and there are many hurdles that need to be overcome before such a solution will be viable for IoT security. However, machine learning cannot be a one-stop solution to IoT security. Machines are good at repetitive tasks, but humans are the best option for applying technology to solve problems. Artificial intelligence may be able to utilize deep learning to deal with small attacks automatically, but human operators still need to teach AI how to cope with more sophisticated attacks. A combination of unsupervised and supervised machine leaning is needed in order to both, deflect attacks and uncover new vulnerabilities.