A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection
With the exponentially evolving trends in technology, IoT networks are vulnerable to serious security issues, allowing intruders to break into networks without authorization and manipulate the data. Their actions can be recognized and avoided by using a system that can detect intrusions. This paper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4050 |
| _version_ | 1797575990734487552 |
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| author | Neeraj Kumar Sanjeev Sharma |
| author_facet | Neeraj Kumar Sanjeev Sharma |
| author_sort | Neeraj Kumar |
| collection | DOAJ |
| description | With the exponentially evolving trends in technology, IoT networks are vulnerable to serious security issues, allowing intruders to break into networks without authorization and manipulate the data. Their actions can be recognized and avoided by using a system that can detect intrusions. This paper presents a hybrid intelligent system and inverted hour-glass-based layered network classifier for feature selection and classification processes, respectively. To accomplish this task, three different datasets have been utilized in the proposed model for identifying old and new attacks. Moreover, a hybrid optimization feature selection technique has been implemented for selecting only those features that can enhance the accuracy of the detection rate. Finally, the classification is performed by using the inverted hour-glass-based layered network model in which data are up-sampled with the increase in the number of layers for effective training. Data up-sampling is performed when small subset of datapoints are observed for any class, which in turn helps in improving the accuracy of the proposed model. The proposed model demonstrated an accuracy of 99.967%, 99.567%, and 99.726% for NSL-KDD, KDD-CUP99, and UNSW NB15 datasets, respectively, which is significantly better than the traditional CNID model. These results demonstrate that our model can detect different attacks with high accuracy and is expected to show good results for new datasets as well. Additionally, to reduce the computational cost of the proposed model, we have implemented it on CPU-based core i3 processors, which are much cheaper than GPU processors. |
| first_indexed | 2024-03-10T21:46:39Z |
| format | Article |
| id | doaj.art-6a791f2293d04385810c542af1ff5984 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:46:39Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6a791f2293d04385810c542af1ff59842023-11-19T14:16:31ZengMDPI AGElectronics2079-92922023-09-011219405010.3390/electronics12194050A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature SelectionNeeraj Kumar0Sanjeev Sharma1School of Information Technology, University Teaching Department, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal 462033, IndiaSchool of Information Technology, University Teaching Department, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal 462033, IndiaWith the exponentially evolving trends in technology, IoT networks are vulnerable to serious security issues, allowing intruders to break into networks without authorization and manipulate the data. Their actions can be recognized and avoided by using a system that can detect intrusions. This paper presents a hybrid intelligent system and inverted hour-glass-based layered network classifier for feature selection and classification processes, respectively. To accomplish this task, three different datasets have been utilized in the proposed model for identifying old and new attacks. Moreover, a hybrid optimization feature selection technique has been implemented for selecting only those features that can enhance the accuracy of the detection rate. Finally, the classification is performed by using the inverted hour-glass-based layered network model in which data are up-sampled with the increase in the number of layers for effective training. Data up-sampling is performed when small subset of datapoints are observed for any class, which in turn helps in improving the accuracy of the proposed model. The proposed model demonstrated an accuracy of 99.967%, 99.567%, and 99.726% for NSL-KDD, KDD-CUP99, and UNSW NB15 datasets, respectively, which is significantly better than the traditional CNID model. These results demonstrate that our model can detect different attacks with high accuracy and is expected to show good results for new datasets as well. Additionally, to reduce the computational cost of the proposed model, we have implemented it on CPU-based core i3 processors, which are much cheaper than GPU processors.https://www.mdpi.com/2079-9292/12/19/4050hybrid intelligent systemsattack detectionIoTartificial learning systemsprediction methods |
| spellingShingle | Neeraj Kumar Sanjeev Sharma A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection Electronics hybrid intelligent systems attack detection IoT artificial learning systems prediction methods |
| title | A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection |
| title_full | A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection |
| title_fullStr | A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection |
| title_full_unstemmed | A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection |
| title_short | A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection |
| title_sort | hybrid modified deep learning architecture for intrusion detection system with optimal feature selection |
| topic | hybrid intelligent systems attack detection IoT artificial learning systems prediction methods |
| url | https://www.mdpi.com/2079-9292/12/19/4050 |
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