Early Detection of DDoS Attacks in SDN using Machine Learning Models

Simple item page
dc.contributor.authorIslam, Refah Rafia
dc.contributor.authorMahmood, Fahim
dc.contributor.authorMosharref, Tabia
dc.date.accessioned2023-01-26T06:54:08Z
dc.date.available2023-01-26T06:54:08Z
dc.date.issued2022-05-30
dc.descriptionSupervised by Dr. Md. Moniruzzaman Assistant Professor, Department of CSE, Islamic University of Technology (IUT), Co-Supervisor, Mr. Faisal Hussain Lecturer, Department of CSE, Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.en_US
dc.description.abstractSoftware Defined Networks (SDN) are programmable networks that can be easily managed with a global understanding of network topology. However, while the software-defined network architecture enhances network resource pooling by separating the control layer from the data layer, this centralized management and control introduces security vulnerabilities into the SDN architecture. One of the most dangerous attacks that the SDN architecture faces is distributed denial of service (DDoS). Aiming at the detection of DDoS attacks under the SDN architecture, this paper proposes faster DDoS attack detection using machine learning based classifier XGBoost which provides higher accuracyen_US
dc.identifier.citation[1] Zhuo Chen, Fu Jiang, Yijun Cheng, Xin Gu, Weirong Liu, and Jun Peng. Xgboost classifier for ddos attack detection and analysis in sdn-based cloud. In 2018 IEEE international conference on big data and smart computing (bigcomp), pages 251–256. IEEE, 2018. [2] Gerard Draper-Gil, Arash Habibi Lashkari, Mohammad Saiful Islam Mamun, and Ali A Ghorbani. Characterization of encrypted and vpn traffic using timerelated. In Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), pages 407–414. sn, 2016. [3] Mahmoud Said Elsayed, Nhien-An Le-Khac, and Anca D Jurcut. Insdn: A novel sdn intrusion dataset. IEEE Access, 8:165263–165284, 2020. [4] Heena Kousar, Mohammed Moin Mulla, Pooja Shettar, and DG Narayan. Detection of ddos attacks in software defined network using decision tree. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), pages 783–788. IEEE, 2021. [5] Seyed Mohammad Mousavi and Marc St-Hilaire. Early detection of ddos attacks against sdn controllers. In 2015 international conference on computing, networking and communications (ICNC), pages 77–81. IEEE, 2015. [6] Huseyin Polat, Onur Polat, and Aydin Cetin. Detecting ddos attacks in software-defined networks through feature selection methods and machine learning models. Sustainability, 12(3):1035, 2020. [7] Kshira Sagar Sahoo, Bata Krishna Tripathy, Kshirasagar Naik, Somula Ramasubbareddy, Balamurugan Balusamy, Manju Khari, and Daniel Burgos. An evolutionary svm model for ddos attack detection in software defined networks. IEEE Access, 8:132502–132513, 2020. [8] Reza Bakhtiari Shohani and Seyed Akbar Mostafavi. Introducing a new linear regression based method for early ddos attack detection in sdn. In 2020 6th 37 International Conference on Web Research (ICWR), pages 126–132. IEEE, 2020. [9] Junyuan Tan, Shan Jing, Lei Guo, and Bin Xiao. Ddos detection method based on gini impurity and random forest in sdn environment. In 2021 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pages 601–606. IEEE, 2021. [10] Liang Tan, Yue Pan, Jing Wu, Jianguo Zhou, Hao Jiang, and Yuchuan Deng. A new framework for ddos attack detection and defense in sdn environment. IEEE Access, 8:161908–161919, 2020. [11] Di Wu, Jie Li, Sajal K Das, Jinsong Wu, Yusheng Ji, and Zhetao Li. A novel distributed denial-of-service attack detection scheme for software defined networking environments. In 2018 IEEE International Conference on Communications (ICC), pages 1–6. IEEE, 2018. [12] Jin Ye, Xiangyang Cheng, Jian Zhu, Luting Feng, and Ling Song. A ddos attack detection method based on svm in software defined network. Security and Communication Networks, 2018, 2018.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1664
dc.language.isoenen_US
dc.publisherDepartment of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.subjectSDN, XGBoost, Random Forest, Decision Tree, KNN, SVM, CICDDoS 2019, DDoSen_US
dc.titleEarly Detection of DDoS Attacks in SDN using Machine Learning Modelsen_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
Rafia_Thesis2022_CSE_170041051_170041027_170041010 - Fahim Mahmood, 170041027.pdf
Size:
1.38 MB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
Rafia _25% Thesis2022_TurnitinReport_CSE_170041051_170041027_170041010 - Fahim Mahmood, 170041027.pdf
Size:
34.63 KB
Format:
Adobe Portable Document Format
Description:
Turnitin Similarity Report, Overall Similarity 25%
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

2022