A Machine Learning Based Detection and Mitigation of the DDOS Attack by Using SDN Controller Framework

Revathi, M.; Ramalingam, V.; Amutha, B.

doi:10.1007/s11277-021-09071-1

Cited by 30 publications

(19 citation statements)

References 38 publications

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“…Centralize SDN controller functions through seamless connections to all the forwarding devices. Centralized SDN architecture provisions robust implementation of network optimization strategies at the application layer such as load balancing and interference mitigation [65]. However, a single centralized SDN controller faces a series of limitations, including bandwidth issues and processing overload in massive communication scenarios.…”

Section: A Centralized Sdn Controllersmentioning

confidence: 99%

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Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

et al. 2023

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The outright aim of Beyond Fifth Generation (B5G) communication is to bring a revolution in Quality of Service (QoS) through enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC). Thereby, due to this massive expansion in the communication paradigm, interference is bound to surface unexpected challenges in the wireless domain. Thus, interference mitigation is a foundational aim in the research paraphernalia of B5G communication. Presently, Software Defined Networking (SDN) is emerging to empower wireless communication with centralized routing-based interference mitigation. With this premise, we aim to present a focused review on SDN-based interference mitigation in wireless communication. Initially, we discuss current research horizon in the subject domain and briefly explore literature for routing-based interference mitigation. Thereby, we analyze the potential of SDN through evaluation of existing solutions for interference mitigation in B5G communication. We conclude our survey by highlighting prominent future research directions in SDN-based interference mitigation.

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Section: A Centralized Sdn Controllersmentioning

confidence: 99%

“…Ryu is widely used to implement various SDN-based interference mitigation schemes, such as ML-based interference mitigation [65] and NS in 5G [69]. In [70], the authors suggested the addition of middleware in Ryu SDN controller for the optimized transport layer management in 5G networks.…”

Section: A Centralized Sdn Controllersmentioning

confidence: 99%

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

et al. 2023

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“…Additional papers [ 23 , 24 , 25 , 26 , 27 , 31 , 32 , 33 , 34 ] presented other approaches to detect DDoS attacks. These include using deep learning [ 25 ], traffic authentication [ 26 ], a cascaded federated deep learning framework [ 25 ], and artificial intelligence merged methods [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Bakar

Huang

Javed

et al. 2023

Sensors

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Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

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“…Researchers have recently employed advanced methods such as deep learning to enable tracing the source of an attack [21]. This, in turn, will facilitate specifically targeting the source and identify the type of denial-of-service attack.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of software-defined network controllers in terms of network forensics processes and capabilities

ÇİL

2024

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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The proliferation of software-defined networks (SDN) increases the necessity of security and forensic research in this field. Network forensics is of particular importance considering the ever-increasing traffic density and variety of devices, and SDN has great potential for improved forensic processes thanks to its ability to provide a centralized view and control of the network. This article's motivation is the lack of a standard forensic process in SDN. The main objective of this study is to examine the differences in the forensic processes of different SDN controllers, whether the southbound interface data is sufficient for the forensic processes, and whether it is possible to choose the best controller in terms of forensics. Four of the most widely used controllers have been selected and tested under seven different scenarios to observe how the results were obtained in terms of forensics. During the tests, in addition to the routine data accesses, attack preparation tools and denial-of-service attack tools were used to expand the scope. Experiments in which each scenario was applied for four different controllers demonstrated that different controllers have different characteristics in network forensics parameters, such as attack type detection, attacker information, service interruptions, packet size, and the number of packets. Experiments proved that southbound interface data is sufficient for forensic processes, different controllers have different characteristics in forensic processes, none of the most used controllers is the best to cover all forensic processes, and a standard forensic method is required for software-defined network forensics.

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A Machine Learning Based Detection and Mitigation of the DDOS Attack by Using SDN Controller Framework

Cited by 30 publications

References 38 publications

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

A comparative analysis of software-defined network controllers in terms of network forensics processes and capabilities

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