An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT
oday, due to the considerable benefits of the Internet of Things (IoT) in various fields such as smart homes, industry, cars, agriculture, etc., its application is very widespread. Due to this, the security of these networks is receiving more and more attention. One of the methods of providing secur...
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| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
| Published: |
Semnan University
2024-04-01
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| Series: | مجله مدل سازی در مهندسی |
| Subjects: | |
| Online Access: | https://modelling.semnan.ac.ir/article_8366_982118b276761e251a8104e4ea1c48ee.pdf |
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| Summary: | oday, due to the considerable benefits of the Internet of Things (IoT) in various fields such as smart homes, industry, cars, agriculture, etc., its application is very widespread. Due to this, the security of these networks is receiving more and more attention. One of the methods of providing security in networks as well as IoT network is intrusion detection systems. Traditional intrusion detection systems are not very efficient for use in the Internet of Things, so the use of new methods is required. One of these methods is intrusion detection systems based on machine learning and deep learning that have been considered in this area. They are trained in machine learning and deep neural network learning to detect attack patterns. There are important parameters for setting up a machine learning network, and choosing the right value for these parameters has a great impact on system accuracy. In this paper, a method is presented that uses meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and gray wolf to find the optimal hyperparameters for the deep learning network and the intrusion detection system is created based on these hyperparameters. This method was implemented using the Tensorflow and keras libraries and tested on the KDDCup99, UNSW-NB15 and Bot-IoT datasets. The results showed that the proposed method can detect attacks with a high accuracy of 99%.. |
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| ISSN: | 2008-4854 2783-2538 |