Computers and mobile devices running rich operating systems have a plethora of security solutions and encryption protocols that can protect them against the multitude of threats they face as soon as they become connected to the Internet. Such is not the case with IoT.
Of the billions of IoT devices presently in use, a considerable percentage are sporting low-end processing power and storage capacity and don’t have the capability to become extended with security solutions. Yet they are connected to the Internet, nonetheless, which is an extremely hostile environment.
Basically, it’s like going to the battlefield without armor.
That’s why new IoT vulnerabilities are constantly surfacing, and countless IoT devices are falling victim to hacks, botnets and other evil deeds every day. It takes mere minutes for a malicious hacker to find thousands of vulnerable devices in the search engine Shodan, and compromised IoT devices frequently become beachheads for more serious hacks in networks. The bottom line is that too many of our smart devices are inherently too dumb to protect themselves (and us) against cyberattacks.
But this is a gap that can be bridged with machine learning and analytics, especially as it is becoming more readily available to developers and manufacturers.
IoT devices are generating tons of data, and machine learning is being employed to analyze and peruse that data to help improve efficiency and customer service, and reduce costs and energy consumption. The same mechanics can be employed in security-related use cases, such as determining safe device behavior and general usage patterns, which can subsequently help to spot and block abnormal activity and potentially harmful behavior.
Already, several tech firms are drawing on this to offer solutions that enhance IoT security,especially in smart homes, where there are no defined security standards and practices.