Method for detecting abnormal behaviors of Internet of Things based on machine learning technology

Abstract

The invention discloses a method for detecting abnormal behaviors of the Internet of Things based on a machine learning technology. The method comprises the following steps: A, collecting protocol data of the Internet of Things; b, performing feature processing on the collected data, and distinguishing whether the data belongs to a numerical type or a character type according to key values in thedata; if the character type belongs to the numeric type, entering the step C, and if the character type belongs to the character type, entering the step D; C, performing periodic behavior calculationon the data belonging to the numerical type, modeling by utilizing a machine learning algorithm, and predicting abnormal data; and D, performing word segmentation processing on the data belonging to the character type to judge the character entropy, performing machine learning algorithm modeling, and calculating outliers so as to distinguish abnormal data. According to the method, the normal flowcharacteristics of the Internet of Things are analyzed by utilizing a machine learning algorithm, so that the flow behavior is predicted.

Description

technical field [0001] The invention relates to the technical field of Internet of Things security, in particular to a method for detecting abnormal behavior of the Internet of Things based on machine learning technology. Background technique [0002] In the field of IoT, the vast majority of threats currently come from hardware cracking or near-field communication attacks on IoT terminals. At the level of communication between devices and IoT clouds, there are almost no IoT attack threats, so there is no corresponding response. Threat detection and identification are carried out according to the rules, so that the threats hidden in the communication protocol of the Internet of Things cannot be discovered. [0003] Therefore, a method for detecting abnormal behavior of the Internet of Things based on machine learning technology can be formulated, using machine learning algorithms to analyze the normal traffic characteristics of the Internet of Things, and predict the traffic...

AI Technical Summary

Problems solved by technology

[0002] In the field of IoT, the vast majority of threats currently come from hardware cracking or near-field communication attacks on IoT terminals. At the level of communication between devices and IoT clouds, there are almost no IoT attack threats, so there is no corresponding response. Threat detection and identification based on the rules of the Internet of Things, resulting in the failure to discover the threats hidden in the communication protocol of the Internet of Things