Contrary to popular belief, most IoT devices in the market do not utilize the best encryption methods and security protocols, and thus aren’t well equipped to deter any security threats. However, many of them are incapable of upgrading themselves, simply because they weren’t meant to be very secure in the first place.
It is a known fact that, regardless of their high rate of adoption across the globe, more than 85% of the world’s IoT devices are not secure. Frankly speaking, IoT is better placed in the world of business enterprises, where devices are capable of improving security and reliability aspects. But in the consumer world, where affordability holds a higher position than security, manufacturers surely cannot be trusted with security. Therefore in such situations, many upcoming IoT devices will be more prone to botnets and other attacks than ever. Thankfully, we can solve this problem if we use analytics and machine learning in improving IoT security.
Currently, machine learning is used to analyze IoT-generated data to enhance user experience and efficiency. The same technology can be used to improve IoT security practices by analyzing usage patterns and device behavior. It can help you to block abnormal activities and potential threats. Gladly, technologists are now focusing on tweaking the most vulnerable IoT security i.e. at home.
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Using Cloud To Centralize Intelligence
Scientists are now trying to aggregate data from all endpoints of IoT products inside a cloud server. It will help them to analyze inputs and detect malicious behavior. They will also be able to see which servers and devices are communicating with IoT devices and hence, spot an abnormal behavior. They can check for suspicious packets, misleading URLs and malicious downloads.
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Using Human-Aided Intelligence With Machine Learning
Machine learning can be beneficial in developing Augmented Intelligence to secure IoT devices A security system based on just pattern recognition and machine learning will only gather information from existing connections i.e already connected devices and network. Anything external will be seen as a threat. Thus, such systems will trigger false alarms every now and then. The best way to mitigate it is to induce augmented intelligence (human intelligence with machine learning).
Human intelligence can easily differentiate between benign and malicious activities. Further, human feedbacks can be imitated in future to prevent false alarms. Hence, the model enhances threat detection efficiency and eventually decrease false alarms.
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Help From IoT Behavior
Luckily, IoT devices are designed only to perform a definite range of functions. Hence, a well balanced mixture of human intelligence and machine learning can easily detect and stop a malicious behavior.
Image Source: wired.com
The model consists of a small device that can be easily installed in home networks, a mobile application that permits user to manage the device, and a cloud service that stores and analyzes the consolidated data through machine learning algorithms. Such model improves its precision over time as much as it collects information from devices and customers.
At last, machine learning alone cannot be considered as a complete solution. It needs to be combined with human intelligence to stop attacks.