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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Semnan University
2024-04-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_8366_982118b276761e251a8104e4ea1c48ee.pdf |
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| author | Bahman Sanjabi Mahmood Ahmadi |
| author_facet | Bahman Sanjabi Mahmood Ahmadi |
| author_sort | Bahman Sanjabi |
| collection | DOAJ |
| description | 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%.. |
| format | Article |
| id | doaj-art-09ffaef5861e4abfac388d04544b14d1 |
| institution | Kabale University |
| issn | 2008-4854 2783-2538 |
| language | fas |
| publishDate | 2024-04-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj-art-09ffaef5861e4abfac388d04544b14d12025-01-15T08:14:58ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382024-04-012276698310.22075/jme.2023.30503.24438366An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOTBahman Sanjabi0Mahmood Ahmadi1Master's degree in Computer Architecture Engineering, Department of Computer Engineering and Information Technology, Razi University, IranAssociate Professor, Department of Computer Engineering and Information Technology, Razi University, Iranoday, 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%..https://modelling.semnan.ac.ir/article_8366_982118b276761e251a8104e4ea1c48ee.pdfdeep learninginrusion detection systemsinternet of thingsmeta-heuristic algorithmsgeray wolf optimizer |
| spellingShingle | Bahman Sanjabi Mahmood Ahmadi An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT مجله مدل سازی در مهندسی deep learning inrusion detection systems internet of things meta-heuristic algorithms geray wolf optimizer |
| title | An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT |
| title_full | An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT |
| title_fullStr | An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT |
| title_full_unstemmed | An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT |
| title_short | An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT |
| title_sort | intrusion detection system based on deep learning and metaheuristic algorithm for iot |
| topic | deep learning inrusion detection systems internet of things meta-heuristic algorithms geray wolf optimizer |
| url | https://modelling.semnan.ac.ir/article_8366_982118b276761e251a8104e4ea1c48ee.pdf |
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