A Hybrid Method for Intrusion Detection in the IOT
In computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS 2018, UNS...
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| Format: | Article |
| Language: | English |
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University of science and culture
2022-07-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_164090_8928c851fcac701e6101be8943c27535.pdf |
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| author | Hossein Faghih Aliabadi |
| author_facet | Hossein Faghih Aliabadi |
| author_sort | Hossein Faghih Aliabadi |
| collection | DOAJ |
| description | In computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS 2018, UNSW-NB15 and MQTT datasets have been used. The evaluation criteria include precision, accuracy, F1 score, and, readability. The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. This algorithm showed the highest known accuracy of 99% on the NSL-KDD dataset. It has reached 97% in all criteria in UNSW-NB15, 96% in all criteria in CIC-IDS 2018, and also, in MQTT for three abstraction levels of features, i.e. packet-based flow features, unidirectional flow, and The two-way flow has reached above 97%, which shows the superiority of this algorithm. |
| format | Article |
| id | doaj-art-17e6c7deea60443d82be2e7ded3419e6 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-17e6c7deea60443d82be2e7ded3419e62025-01-04T09:54:30ZengUniversity of science and cultureInternational Journal of Web Research2645-43432022-07-0152546010.22133/ijwr.2022.370774.1143A Hybrid Method for Intrusion Detection in the IOTHossein Faghih Aliabadi 0MSC.Computer Networks, Faculty of Electricity, Computer and Advanced Technologies of Urmia University, IranIn computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS 2018, UNSW-NB15 and MQTT datasets have been used. The evaluation criteria include precision, accuracy, F1 score, and, readability. The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. This algorithm showed the highest known accuracy of 99% on the NSL-KDD dataset. It has reached 97% in all criteria in UNSW-NB15, 96% in all criteria in CIC-IDS 2018, and also, in MQTT for three abstraction levels of features, i.e. packet-based flow features, unidirectional flow, and The two-way flow has reached above 97%, which shows the superiority of this algorithm.https://ijwr.usc.ac.ir/article_164090_8928c851fcac701e6101be8943c27535.pdfinternet of thingsintrusion detection systemhybrid systemdeep learning introduction |
| spellingShingle | Hossein Faghih Aliabadi A Hybrid Method for Intrusion Detection in the IOT International Journal of Web Research internet of things intrusion detection system hybrid system deep learning introduction |
| title | A Hybrid Method for Intrusion Detection in the IOT |
| title_full | A Hybrid Method for Intrusion Detection in the IOT |
| title_fullStr | A Hybrid Method for Intrusion Detection in the IOT |
| title_full_unstemmed | A Hybrid Method for Intrusion Detection in the IOT |
| title_short | A Hybrid Method for Intrusion Detection in the IOT |
| title_sort | hybrid method for intrusion detection in the iot |
| topic | internet of things intrusion detection system hybrid system deep learning introduction |
| url | https://ijwr.usc.ac.ir/article_164090_8928c851fcac701e6101be8943c27535.pdf |
| work_keys_str_mv | AT hosseinfaghihaliabadi ahybridmethodforintrusiondetectionintheiot AT hosseinfaghihaliabadi hybridmethodforintrusiondetectionintheiot |