Internet of Things environment situation awareness method based on machine learning

Abstract

An Internet of Things environment situation awareness method based on machine learning comprises two parts of machine learning and situation awareness, the safety problem existing in an Internet of Things distributed network environment is more complex, and a traditional situation awareness method mainly aims at a centralized network and cannot be effectively applied to the Internet of Things environment. Situational safety elements existing in a distributed environment are reasonably extracted; and modeling the extracted situation elements by using a machine learning method, constructing a network safety situation evaluation model of the Internet of Things environment, and analyzing and predicting the safety situation of the environment, thereby assisting safety personnel or users to respond and troubleshoot safety threats in the environment in time.

Description

technical field [0001] The invention belongs to the field related to network security, and relates to a method for situational awareness of the Internet of Things environment based on supervised learning. Background technique [0002] With the development of 5G, the world of Internet of Everything is coming, which means that there will be more and more terminal devices. At the same time, people pay more attention to user experience, which makes the demand for real-time performance increasing day by day, thus giving birth to the development of edge computing. . Under the dual conditions of massive terminal devices and increasingly widespread edge computing applications, the three-tier network of "terminal --> edge server layer --> cloud layer" has become more and more common. Compared with the traditional "terminal --> The addition of the edge layer greatly reduces the consumption of bandwidth and resources, and the cost is getting lower and lower. At the same time,...

AI Technical Summary

Problems solved by technology

According to the Hewlett-Packard Security Research Institute, after conducting security tests on 10 most popular smart devices, it was found that the following five major security risks appear on almost all smart devices: 80% of IOT devices have the risk of privacy leakage or abuse; 80% of IOT devices are allowed to use weak passwords; 70% of IOT devices communicate with the Internet or LAN without encryption; 60% of IOT devices have security vulnerabilities in their web interfaces; 60% of IOT devices do not use encryption when downloading software updates; the above are only terminals There are problems in the device itself. If so many terminal devices are controlled by hackers to launch DDoS attacks on edge servers or even the cloud, the cloud may be paralyzed in an instant.

Traditional situational awareness is only used for centralized environments, such as a certain server, and does not consider the problems in distributed environments. Even in previous DDoS attacks, PCs were used as bots, and the security of PCs is still superior. Due to the security of IoT terminals, attacks in the new environment are easier than before, so it is imperative to study the situational awareness system in the new environment

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