Distributed intrusion detection method based on multilayer extreme learning machine in Internet of Things environment

Abstract

The invention discloses a distributed intrusion detection method based on a multi-layer extreme learning machine in an Internet of Things environment, and aims to move tasks with heavier calculation for realizing automatic attack detection to boundary equipment due to the characteristic that resources of related equipment are constrained, so as to enable a processing function to be close to a data source. The boundary devices can operate a preset classification model. However, when facing a large amount of training data, there is no sufficient storage and processing capabilities to construct and upgrade such models. In order to solve the problem, training operation with dense calculation and large storage capacity is moved to a cloud server to be carried out, and a single-hidden-layer extreme learning machine model and a multi-hidden-layer extreme learning machine model are constructed and trained in the cloud server, so that boundary equipment executes flow classification based on a deep learning model preset in the cloud server; therefore, whether the traffic is normal traffic or network attack is classified, and experimental analysis shows that the multi-hidden-layer extreme learning machine has better performance.

Description

technical field [0001] The invention belongs to the field of intrusion detection and deep learning, and in particular relates to a distributed intrusion detection method based on a multi-layer extreme learning machine under the Internet of Things environment. Background technique [0002] The rapid development of Internet of Things technology is rapidly bridging the gap between traditional information services and the surrounding physical environment through the connection of increasingly complex sensing devices and Internet-based remote control devices. Many potential IoT application services have emerged, such as environmental monitoring, traffic monitoring, health care monitoring, etc. These applications have greatly improved the interaction between humans and computing devices. The growing Internet of Things applications and physical information services have increasingly high requirements for network security. Intrusion detection under the Internet of Things has become ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem in the prior art that the border devices of distributed intrusion detection in the Internet of Things environment cannot handle too large training data, the present invention provides a distributed intrusion detection method based on multi-layer extreme learning machines in the Internet of Things environment

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