Internet of Things equipment access detection method and system based on SMOTE and parallel random forest

Abstract

The invention discloses an Internet of Things equipment access detection method based on an SMOTE and a parallel random forest. The method comprises the following steps: obtaining a plurality of pieces of equipment fingerprint information of Internet of Things equipment, analyzing each piece of equipment fingerprint information to obtain a feature attribute value corresponding to the equipment fingerprint information, and constructing a feature matrix according to the feature attribute values corresponding to all the obtained equipment fingerprint information; inputting the feature matrix into a trained equipment identification classifier to obtain an output result, and querying a corresponding vulnerability and a type thereof in a local vulnerability library according to the output result; and judging whether the queried vulnerability is a medium-high risk vulnerability or not according to the type, and if so, forbidding access of the Internet of Things equipment, otherwise, allowing the access of the Internet of Things equipment. According to the invention, the technical problem that the detection accuracy of uncommon Internet of Things equipment is low due to the fact that Internet of Things equipment is diversified in variety and large in difference in an existing Internet of Things equipment access detection method based on machine learning or deep learning can be solved.

Description

technical field [0001] The present invention belongs to the technical field of machine learning, and more specifically relates to a method and system for security detection of IoT device access based on SMOTE and parallel random forest. Background technique [0002] In recent years, with the rapid development of IoT technology, IoT devices have been increasingly widely used. Correspondingly, the security issues of IoT devices have become increasingly prominent, and IoT device access detection has become an important research direction in the field of IoT security. [0003] Existing Internet of Things device access detection is based on machine learning or deep learning to identify devices. [0004] However, the above-mentioned existing IoT device access detection methods have some defects that cannot be ignored: First, due to the variety of IoT devices and their great differences, the existing machine learning-based access detection methods will lead to existing The method...

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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method and system for security detection of Internet of Things devices based on SMOTE and parallel random forest. The purpose is to solve the existing problems based on machine learning or deep learning The Internet of Things device access detection method has a variety of IoT devices and great differences, which leads to technical problems such as low detection accuracy for uncommon Internet of Things devices, and when using massive sample data for model training, it takes too much time. prolonged technical issue

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