Development of intrusion detection system using residual feedforward neural network algorithm
An intrusion detection system (IDS) is required to protect data from security threats that infiltrate unwanted information via a regular channel, both during storage and transmission. This detection system must differentiate between normal data and abnormal or hacker-generated data. Additionally, th...
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Online Access: | http://irep.iium.edu.my/96797/2/2021_ISRITI_Cert_Development_of_Intrusion_Detection_System_using.pdf http://irep.iium.edu.my/96797/13/96797_Development%20of%20intrusion%20detection%20system.pdf http://irep.iium.edu.my/96797/ https://edas.info/web/20214thisriti/index.html https://doi.org/10.1109/ISRITI54043.2021.9702773 |
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my.iium.irep.967972022-02-20T08:30:40Z http://irep.iium.edu.my/96797/ Development of intrusion detection system using residual feedforward neural network algorithm Rustam, Rushendra Ramli, Kalamullah Hayati, Nur Ihsanto, Eko Gunawan, Teddy Surya Halbouni, Asmaa Hani TK7885 Computer engineering An intrusion detection system (IDS) is required to protect data from security threats that infiltrate unwanted information via a regular channel, both during storage and transmission. This detection system must differentiate between normal data and abnormal or hacker-generated data. Additionally, the intrusion detection system (IDS) must be precise and quick to analyze real-time traffic data. Despite extensive research, there is still a need to improve detection accuracy and speed due to the tremendous increase in internet traffic volume and variety. This paper introduces a novel, efficient, and accurate approach for real-time intrusion detection and classification based on the Residual Feedforward Neural Network (RFNN) algorithm. The RFNN algorithm is developed to avoid overfitting, improve detection accuracy, and accelerate training and inference. Additionally, the suggested algorithm is highly adaptable and straightforward to accommodate different types of intrusion. The prominent NSL-KDD dataset was utilized for training and testing in this study. The accuracy obtained for two and five classes was 84.7 percent and 90.5 percent, respectively. Additionally, the identification speed was 15 µs and 14 µs, respectively, indicating that real-time detection is feasible. IEEE 2022-02-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/96797/2/2021_ISRITI_Cert_Development_of_Intrusion_Detection_System_using.pdf application/pdf en http://irep.iium.edu.my/96797/13/96797_Development%20of%20intrusion%20detection%20system.pdf Rustam, Rushendra and Ramli, Kalamullah and Hayati, Nur and Ihsanto, Eko and Gunawan, Teddy Surya and Halbouni, Asmaa Hani (2022) Development of intrusion detection system using residual feedforward neural network algorithm. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia. https://edas.info/web/20214thisriti/index.html https://doi.org/10.1109/ISRITI54043.2021.9702773 |
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TK7885 Computer engineering Rustam, Rushendra Ramli, Kalamullah Hayati, Nur Ihsanto, Eko Gunawan, Teddy Surya Halbouni, Asmaa Hani Development of intrusion detection system using residual feedforward neural network algorithm |
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An intrusion detection system (IDS) is required to protect data from security threats that infiltrate unwanted information via a regular channel, both during storage and transmission. This detection system must differentiate between normal data and abnormal or hacker-generated data. Additionally, the intrusion detection system (IDS) must be precise and quick to analyze real-time traffic data. Despite extensive research, there is still a need to improve detection accuracy and speed due to the tremendous increase in internet traffic volume and variety. This paper introduces a novel, efficient, and accurate approach for real-time intrusion detection and classification based on the Residual Feedforward Neural Network (RFNN) algorithm. The RFNN algorithm is developed to avoid overfitting, improve detection accuracy, and accelerate training and inference. Additionally, the suggested algorithm is highly adaptable and straightforward to accommodate different types of intrusion. The prominent NSL-KDD dataset was utilized for training and testing in this study. The accuracy obtained for two and five classes was 84.7 percent and 90.5 percent, respectively. Additionally, the identification speed was 15 µs and 14 µs, respectively, indicating that real-time detection is feasible. |
format |
Conference or Workshop Item |
author |
Rustam, Rushendra Ramli, Kalamullah Hayati, Nur Ihsanto, Eko Gunawan, Teddy Surya Halbouni, Asmaa Hani |
author_facet |
Rustam, Rushendra Ramli, Kalamullah Hayati, Nur Ihsanto, Eko Gunawan, Teddy Surya Halbouni, Asmaa Hani |
author_sort |
Rustam, Rushendra |
title |
Development of intrusion detection system using residual feedforward neural network algorithm |
title_short |
Development of intrusion detection system using residual feedforward neural network algorithm |
title_full |
Development of intrusion detection system using residual feedforward neural network algorithm |
title_fullStr |
Development of intrusion detection system using residual feedforward neural network algorithm |
title_full_unstemmed |
Development of intrusion detection system using residual feedforward neural network algorithm |
title_sort |
development of intrusion detection system using residual feedforward neural network algorithm |
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IEEE |
publishDate |
2022 |
url |
http://irep.iium.edu.my/96797/2/2021_ISRITI_Cert_Development_of_Intrusion_Detection_System_using.pdf http://irep.iium.edu.my/96797/13/96797_Development%20of%20intrusion%20detection%20system.pdf http://irep.iium.edu.my/96797/ https://edas.info/web/20214thisriti/index.html https://doi.org/10.1109/ISRITI54043.2021.9702773 |
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1725972477329276928 |
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13.15806 |