Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning
The rapid evaluation of smart cities has revolutionized the research and development field to a very extensive level which presents challenges in handling massive amounts of data. However, the integration of IoT into various aspects of life has introduced various challenges related to the security a...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10689394/ |
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| author | Emad-Ul-Haq Qazi Tanveer Zia Muhammad Hamza Faheem Khurram Shahzad Muhammad Imran Zeeshan Ahmed |
| author_facet | Emad-Ul-Haq Qazi Tanveer Zia Muhammad Hamza Faheem Khurram Shahzad Muhammad Imran Zeeshan Ahmed |
| author_sort | Emad-Ul-Haq Qazi |
| collection | DOAJ |
| description | The rapid evaluation of smart cities has revolutionized the research and development field to a very extensive level which presents challenges in handling massive amounts of data. However, the integration of IoT into various aspects of life has introduced various challenges related to the security and privacy of IoT systems. IoT sensors capture large volumes of sensitive customer data, which can potentially make them a target and pose serious threats, including financial loss and identity theft. Strong intrusion detection systems are essential for protecting networked, data-driven ecosystems from potential cyber threats. In this paper, we propose a novel deep learning-based approach that focuses on emerging zero-touch networks that autonomously manage network resources to ensure network security, the proposed approach identifies various network intrusions such as DDoS, Botnet, Brute force, and Infiltration. Our proposed approach presents a major improvement in IoT security. We have used the CICIDS-2018 benchmark dataset and propose a deep learning-based network intrusion detection System for Zero Touch Networks (DL-NIDS-ZTN). The proposed study utilizes convolutional neural networks that correctly identify benign and malicious traffic and achieve 99.80% accuracy with the CICIDS-2018 dataset. By implementing the DL-NIDS-ZTN methodology, we aim to strengthen the security framework of smart cities and ensure the secure and seamless integration of IoT. |
| format | Article |
| id | doaj-art-cc481d72fd2a448f83ffbc0c1fbe121b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cc481d72fd2a448f83ffbc0c1fbe121b2025-08-20T02:47:39ZengIEEEIEEE Access2169-35362024-01-011214162514163810.1109/ACCESS.2024.346647010689394Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep LearningEmad-Ul-Haq Qazi0https://orcid.org/0000-0003-1448-3632Tanveer Zia1Muhammad Hamza Faheem2https://orcid.org/0000-0002-1643-6728Khurram Shahzad3https://orcid.org/0000-0001-8430-0518Muhammad Imran4Zeeshan Ahmed5Center of Excellence in Cybercrimes and Digital Forensics, Naif Arab University for Security Sciences, Riyadh, Saudi ArabiaCenter of Excellence in Cybercrimes and Digital Forensics, Naif Arab University for Security Sciences, Riyadh, Saudi ArabiaCenter of Excellence in Cybercrimes and Digital Forensics, Naif Arab University for Security Sciences, Riyadh, Saudi ArabiaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, AustraliaInstitute of Innovation, Science, and Sustainability, Federation University Australia, Berwick, VIC, AustraliaInstitute of System Engineering, Riphah International University, Islamabad, PakistanThe rapid evaluation of smart cities has revolutionized the research and development field to a very extensive level which presents challenges in handling massive amounts of data. However, the integration of IoT into various aspects of life has introduced various challenges related to the security and privacy of IoT systems. IoT sensors capture large volumes of sensitive customer data, which can potentially make them a target and pose serious threats, including financial loss and identity theft. Strong intrusion detection systems are essential for protecting networked, data-driven ecosystems from potential cyber threats. In this paper, we propose a novel deep learning-based approach that focuses on emerging zero-touch networks that autonomously manage network resources to ensure network security, the proposed approach identifies various network intrusions such as DDoS, Botnet, Brute force, and Infiltration. Our proposed approach presents a major improvement in IoT security. We have used the CICIDS-2018 benchmark dataset and propose a deep learning-based network intrusion detection System for Zero Touch Networks (DL-NIDS-ZTN). The proposed study utilizes convolutional neural networks that correctly identify benign and malicious traffic and achieve 99.80% accuracy with the CICIDS-2018 dataset. By implementing the DL-NIDS-ZTN methodology, we aim to strengthen the security framework of smart cities and ensure the secure and seamless integration of IoT.https://ieeexplore.ieee.org/document/10689394/Intrusion detectionzero-touch networkssmart cityIoTdeep learningconvolutional neural networks |
| spellingShingle | Emad-Ul-Haq Qazi Tanveer Zia Muhammad Hamza Faheem Khurram Shahzad Muhammad Imran Zeeshan Ahmed Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning IEEE Access Intrusion detection zero-touch networks smart city IoT deep learning convolutional neural networks |
| title | Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning |
| title_full | Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning |
| title_fullStr | Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning |
| title_full_unstemmed | Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning |
| title_short | Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning |
| title_sort | zero touch network security ztns a network intrusion detection system based on deep learning |
| topic | Intrusion detection zero-touch networks smart city IoT deep learning convolutional neural networks |
| url | https://ieeexplore.ieee.org/document/10689394/ |
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