The Internet of Things and its security implications are consistently making headlines and remain a growing topic of concern for smart home device owners and businesses alike. According to a recent study, 39 percent of executives answered that security and privacy was their number one concern about investing in IoT. The reasons for security breaks and solutions for the issue could easily transform into an entire dissertation, starting with the importance of security in IoT and ending with ideas about how it can be achieved. I want to break it down by addressing some misconceptions about the security challenge and how machine learning, which is starting to exist within the smart home, can be part of the solution to the issue.
There are many everyday accounts and programs that can be compromised by hackers. Do you feel that your bank account is secure? How about your email, Facebook and Twitter accounts? Is your wireless connection secure? If you answered “yes” to these questions, then your answer about IoT-enabled devices being secure should also be “yes.” At the very least, most of us trust the companies that we choose to do business with to protect us against security threats, and it should be the same way with smart home devices.
Machine learning is an emerging trend in connected devices that also contributes to more secure networks. The adaptive learning algorithm benefits the consumer by anticipating the needs of the connected device owner and making suggestions for rules based on the user’s habits. For example, machine learning allows a device to learn the routine of its user, such as the time they get home or go to sleep, and then suggests rules based on those behaviors for all connected devices to better work together. This intricate process allows homes to adapt to you and is the difference between a proactive and reactive device.
This smart home feature has the potential to reduce security breaches by identifying risks and detecting them faster. Because machine learning learns the habits of its user, it can detect patterns or behaviors that are out of the ordinary, predicting risks and intrusions before they happen.
Of course, the most effective way to improve IoT security is to take proper preventative measures from the very beginning, like critical software in other industries. This also includes investments in specialized persons, third-party software and prevention companies. In the hurry to be IoT-enabled, many companies don’t place security as a top priority, but the reality is that connected devices are useless without proper security methods. In fact, lack of preventative measures could keep the IoT industry from becoming mainstream.
The future of the smart home includes making the experience as easy and seamless as possible for its user, and one of the large components of its evolution will be machine learning. The good news is that this emerging trend also has the capability to help smart home owners against the upcoming obstacle and concern of privacy and security.
Eduardo Pinheiro started out working at his University's computer science research center, and then became a developer in the first Portuguese Linux distribution. He created his first company in 2002 in the Information Systems area and then went on to create two more companies in the subsequent years. Eduardo and co-founder Domingo Bruges were inspired to start Muzzley, an IoT platform, featuring machine-learning technology that allows people to control all of their smart home devices in one app, after a visit to Silicon Valley.
