Low rate DoS attack detection in IoT - SDN using deep learning
The lack of standardization and the heterogeneous nature of IoT, exacerbated the issue of security and privacy. In recent literature, to improve security at the network level, the possibility of using SDN for IoT networks was explored. An LR DoS attack is an insidious DoS attack that hinders the ava...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/167111 |
| _version_ | 1811681234225463296 |
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| author | Ilango, Harun Surej Ma, Maode Su, Rong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ilango, Harun Surej Ma, Maode Su, Rong |
| author_sort | Ilango, Harun Surej |
| collection | NTU |
| description | The lack of standardization and the heterogeneous nature of IoT, exacerbated the issue of security and privacy. In recent literature, to improve security at the network level, the possibility of using SDN for IoT networks was explored. An LR DoS attack is an insidious DoS attack that hinders the availability of the network to its legitimate users. LR DoS attacks are difficult to detect and can be deadly to a network due to their hidden nature. Recently, the possibility of using ML or DL algorithms to detect LR DoS attacks have gained traction due to advancements in computing technology. The ML and DL algorithms that are currently available in the literature have a detection rate of 95 percent at best. In this work, a novel deep learning scheme called FFCNN is proposed to detect LR DoS attacks in a SDN environment. The CIC DoS 2017 and CIC IDS 2017 datasets provided by the Canadian Institute of Cybersecurity were used for the experimental analysis. The empirical analysis of the proposed algorithm shows that it outperforms the existing machine learning based algorithms. FFCNN promises a lower false alarm rate and better detection rate in the detection of LR DoS. |
| first_indexed | 2024-10-01T03:37:42Z |
| format | Conference Paper |
| id | ntu-10356/167111 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:37:42Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1671112023-05-12T15:39:55Z Low rate DoS attack detection in IoT - SDN using deep learning Ilango, Harun Surej Ma, Maode Su, Rong School of Electrical and Electronic Engineering 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) Engineering Engineering::Electrical and electronic engineering Engineering::Electrical and electronic engineering::Wireless communication systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Internet of Things Software Defined Networking Deep Learning Low-Rate DoS Attacks Network Security CIC DoS 2017 CIC IDS 2017 The lack of standardization and the heterogeneous nature of IoT, exacerbated the issue of security and privacy. In recent literature, to improve security at the network level, the possibility of using SDN for IoT networks was explored. An LR DoS attack is an insidious DoS attack that hinders the availability of the network to its legitimate users. LR DoS attacks are difficult to detect and can be deadly to a network due to their hidden nature. Recently, the possibility of using ML or DL algorithms to detect LR DoS attacks have gained traction due to advancements in computing technology. The ML and DL algorithms that are currently available in the literature have a detection rate of 95 percent at best. In this work, a novel deep learning scheme called FFCNN is proposed to detect LR DoS attacks in a SDN environment. The CIC DoS 2017 and CIC IDS 2017 datasets provided by the Canadian Institute of Cybersecurity were used for the experimental analysis. The empirical analysis of the proposed algorithm shows that it outperforms the existing machine learning based algorithms. FFCNN promises a lower false alarm rate and better detection rate in the detection of LR DoS. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This research is supported by A*STAR, Singapore under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund – Pre Positioning (IAF-PP) (Award A19D6a0053). 2023-05-11T06:06:41Z 2023-05-11T06:06:41Z 2021 Conference Paper Ilango, H. S., Ma, M. & Su, R. (2021). Low rate DoS attack detection in IoT - SDN using deep learning. 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 115-120. https://dx.doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00031 9781665417624 https://hdl.handle.net/10356/167111 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00031 2-s2.0-85127386726 115 120 en A19D6a0053 © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00031. application/pdf |
| spellingShingle | Engineering Engineering::Electrical and electronic engineering Engineering::Electrical and electronic engineering::Wireless communication systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Internet of Things Software Defined Networking Deep Learning Low-Rate DoS Attacks Network Security CIC DoS 2017 CIC IDS 2017 Ilango, Harun Surej Ma, Maode Su, Rong Low rate DoS attack detection in IoT - SDN using deep learning |
| title | Low rate DoS attack detection in IoT - SDN using deep learning |
| title_full | Low rate DoS attack detection in IoT - SDN using deep learning |
| title_fullStr | Low rate DoS attack detection in IoT - SDN using deep learning |
| title_full_unstemmed | Low rate DoS attack detection in IoT - SDN using deep learning |
| title_short | Low rate DoS attack detection in IoT - SDN using deep learning |
| title_sort | low rate dos attack detection in iot sdn using deep learning |
| topic | Engineering Engineering::Electrical and electronic engineering Engineering::Electrical and electronic engineering::Wireless communication systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Internet of Things Software Defined Networking Deep Learning Low-Rate DoS Attacks Network Security CIC DoS 2017 CIC IDS 2017 |
| url | https://hdl.handle.net/10356/167111 |
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