Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network
Computer network technology is growing rapidly, but cyberattacks are also increasing in number and variants that occur every year. Anomaly-based network intrusion detection system (NIDS) is still vulnerable to false positive rates even though it has used a machine learning approach to detect zero-da...
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2023
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Online Access: | https://repository.ugm.ac.id/280306/1/Handika_MIPA.pdf https://repository.ugm.ac.id/280306/ https://ieeexplore.ieee.org/document/10037425 |
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id-ugm-repo.2803062023-11-10T05:50:19Z https://repository.ugm.ac.id/280306/ Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network Handika, Vian Istiyanto, Jazi Eko Ashari, Ahmad Purnama, Satriawan Rasyid Rochman, Syafiqur Dharmawan, Andi Information and Computing Sciences Mathematics and Applied Sciences Computer network technology is growing rapidly, but cyberattacks are also increasing in number and variants that occur every year. Anomaly-based network intrusion detection system (NIDS) is still vulnerable to false positive rates even though it has used a machine learning approach to detect zero-day attacks on network traffic. Deep learning can provide advanced solutions to this problem. However, deep learning requires special handling to process tabular NIDS datasets with highly sparse categorical and numerical data. To overcome this, we propose a new embedding method implemented by embedding not only categorical data but also numerical data to provide the best representation features for deep learning models. The proposed method was evaluated with other deep learning and machine learning models with results outperforming all models based on the f1-score macro using the CSE-CIC-IDS-2018 dataset. 2023-02-09 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/280306/1/Handika_MIPA.pdf Handika, Vian and Istiyanto, Jazi Eko and Ashari, Ahmad and Purnama, Satriawan Rasyid and Rochman, Syafiqur and Dharmawan, Andi (2023) Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network. In: International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022, 22-23 November 2022, Surabaya, Indonesia. https://ieeexplore.ieee.org/document/10037425 |
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Information and Computing Sciences Mathematics and Applied Sciences Handika, Vian Istiyanto, Jazi Eko Ashari, Ahmad Purnama, Satriawan Rasyid Rochman, Syafiqur Dharmawan, Andi Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network |
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Computer network technology is growing rapidly, but cyberattacks are also increasing in number and variants that occur every year. Anomaly-based network intrusion detection system (NIDS) is still vulnerable to false positive rates even though it has used a machine learning approach to detect zero-day attacks on network traffic. Deep learning can provide advanced solutions to this problem. However, deep learning requires special handling to process tabular NIDS datasets with highly sparse categorical and numerical data. To overcome this, we propose a new embedding method implemented by embedding not only categorical data but also numerical data to provide the best representation features for deep learning models. The proposed method was evaluated with other deep learning and machine learning models with results outperforming all models based on the f1-score macro using the CSE-CIC-IDS-2018 dataset. |
format |
Conference or Workshop Item PeerReviewed |
author |
Handika, Vian Istiyanto, Jazi Eko Ashari, Ahmad Purnama, Satriawan Rasyid Rochman, Syafiqur Dharmawan, Andi |
author_facet |
Handika, Vian Istiyanto, Jazi Eko Ashari, Ahmad Purnama, Satriawan Rasyid Rochman, Syafiqur Dharmawan, Andi |
author_sort |
Handika, Vian |
title |
Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network |
title_short |
Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network |
title_full |
Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network |
title_fullStr |
Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network |
title_full_unstemmed |
Feature Representation for Network Intrusion Detection System Trough Embedding Neural Network |
title_sort |
feature representation for network intrusion detection system trough embedding neural network |
publishDate |
2023 |
url |
https://repository.ugm.ac.id/280306/1/Handika_MIPA.pdf https://repository.ugm.ac.id/280306/ https://ieeexplore.ieee.org/document/10037425 |
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1783956211890126848 |