Intrusion detection for IoT based on improved genetic algorithm and deep belief network

Li, Peisong, Zhang, Y and Wang, Xinheng ORCID: (2019) Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7. pp. 31711-31722. ISSN 2169-3536

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With the advent of the Internet of Things, the network security of the transport layer in the Internet of Things is getting more and more attention. Traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of the Internet of Things. Therefore, it is extremely urgent to study the intrusion detection system corresponding to today's Internet of Things security. This paper presents an intrusion detection model based on Genetic Algorithm (GA) and Deep Belief Network (DBN). Through multiple iterations of GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate. Finally, the KDDCUP99 data set was used to simulate and evaluate the model and algorithm. Experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the network.

Item Type: Article
Identifier: 10.1109/ACCESS.2019.2903723
Additional Information: (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords: Internet of Things security; Intrusion detection; Deep Belief Network; Genetic Algorithm
Subjects: Computing > Systems > Computer networking
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Depositing User: Henry Wang
Date Deposited: 08 Mar 2019 14:19
Last Modified: 06 Feb 2024 15:59


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