Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/8/258 |
| _version_ | 1797432545876377600 |
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| author | Jovana Mijalkovic Angelo Spognardi |
| author_facet | Jovana Mijalkovic Angelo Spognardi |
| author_sort | Jovana Mijalkovic |
| collection | DOAJ |
| description | Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intrusions have become more frequent and less detectable. The increase in complexity pushed researchers to boost NIDS effectiveness by introducing machine learning (ML) and deep learning (DL) techniques. However, even with the addition of ML and DL, some issues still need to be addressed: high false negative rates and low attack predictability for minority classes. Aim of the study was to address these problems that have not been adequately addressed in the literature. Firstly, we have built a deep learning model for network intrusion detection that would be able to perform both binary and multiclass classification of network traffic. The goal of this base model was to achieve at least the same, if not better, performance than the models observed in the state-of-the-art research. Then, we proposed an effective refinement strategy and generated several models for lowering the FNR and increasing the predictability for the minority classes. The obtained results proved that using the proper parameters is possible to achieve a satisfying trade-off between FNR, accuracy, and detection of the minority classes. |
| first_indexed | 2024-03-09T10:03:04Z |
| format | Article |
| id | doaj.art-0122bfab5a294deb86599a67cbd60b22 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T10:03:04Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-0122bfab5a294deb86599a67cbd60b222023-12-01T23:17:25ZengMDPI AGAlgorithms1999-48932022-07-0115825810.3390/a15080258Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection SystemsJovana Mijalkovic0Angelo Spognardi1Department of Computer Science, Sapienza University, 00198 Rome, ItalyDepartment of Computer Science, Sapienza University, 00198 Rome, ItalyNetwork Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intrusions have become more frequent and less detectable. The increase in complexity pushed researchers to boost NIDS effectiveness by introducing machine learning (ML) and deep learning (DL) techniques. However, even with the addition of ML and DL, some issues still need to be addressed: high false negative rates and low attack predictability for minority classes. Aim of the study was to address these problems that have not been adequately addressed in the literature. Firstly, we have built a deep learning model for network intrusion detection that would be able to perform both binary and multiclass classification of network traffic. The goal of this base model was to achieve at least the same, if not better, performance than the models observed in the state-of-the-art research. Then, we proposed an effective refinement strategy and generated several models for lowering the FNR and increasing the predictability for the minority classes. The obtained results proved that using the proper parameters is possible to achieve a satisfying trade-off between FNR, accuracy, and detection of the minority classes.https://www.mdpi.com/1999-4893/15/8/258NIDSdeep learningfalse negative ratemachine learningartificial neural network |
| spellingShingle | Jovana Mijalkovic Angelo Spognardi Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems Algorithms NIDS deep learning false negative rate machine learning artificial neural network |
| title | Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems |
| title_full | Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems |
| title_fullStr | Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems |
| title_full_unstemmed | Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems |
| title_short | Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems |
| title_sort | reducing the false negative rate in deep learning based network intrusion detection systems |
| topic | NIDS deep learning false negative rate machine learning artificial neural network |
| url | https://www.mdpi.com/1999-4893/15/8/258 |
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