A comprehensive survey on low-rate and high-rate DDoS defense approaches in SDN: taxonomy, research challenges, and opportunities

Karnani, Suruchi; Agrawal, Neha; Kumar, Rohit

doi:10.1007/s11042-023-16781-0

Cited by 4 publications

(3 citation statements)

References 125 publications

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“…DDoS attacks manipulate the basic principles of network communication and can target different planes of the SDN network, including the control and data planes. Commonly, DDoS floods the victim with an extensive volume of packets, consuming network resources, including bandwidth or server processing capacity, or using vulnerabilities in network protocols to suspend communication between other parts of the network [17].…”

Section: Specific Attacksmentioning

confidence: 99%

“…At this stage, an attacker has the opportunity to intercept the packet and inject harmful rules or modify the existing ones. Because of this, the majority of packets may be sent in the wrong direction [17] (5) Applications: The applications that are implemented in the SDN control layer include traffic monitoring and classification. These applications are created by third parties to add necessary security requirements.…”

Section: Vulnerable Points In the Sdn Environmentmentioning

confidence: 99%

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A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges

Janabi,

Kanakis,

Johnson

2023

Preprint

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: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

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Section: Specific Attacksmentioning

confidence: 99%

Section: Vulnerable Points In the Sdn Environmentmentioning

confidence: 99%

A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges

Janabi,

Kanakis,

Johnson

2023

Preprint

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“…These attacks are typically orchestrated through a network of compromised devices known as botnets, unleashing a deluge of traffic upon the target [1]. DDoS attacks can wreak havoc on online services, and combating these threats can be exceptionally challenging due to the multifarious sources from which the traffic originates [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Setitra,

Fan,

Agbley

et al. 2023

Network

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In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged as an exceedingly pernicious threat, particularly in the context of network management centered around technologies like Software-Defined Networking (SDN). With the increasing intricacy and sophistication of DDoS attacks, the need for effective countermeasures has led to the adoption of Machine Learning (ML) techniques. Nevertheless, despite substantial advancements in this field, challenges persist, adversely affecting the accuracy of ML-based DDoS-detection systems. This article introduces a model designed to detect DDoS attacks. This model leverages a combination of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) to enhance the performance of ML-based DDoS-detection systems within SDN environments. We propose utilizing the SHapley Additive exPlanations (SHAP) feature-selection technique and employing a Bayesian optimizer for hyperparameter tuning to optimize our model. To further solidify the relevance of our approach within SDN environments, we evaluate our model by using an open-source SDN dataset known as InSDN. Furthermore, we apply our model to the CICDDoS-2019 dataset. Our experimental results highlight a remarkable overall accuracy of 99.95% with CICDDoS-2019 and an impressive 99.98% accuracy with the InSDN dataset. These outcomes underscore the effectiveness of our proposed DDoS-detection model within SDN environments compared to existing techniques.

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Enhancing Network Security in SDN: Detecting Low-Rate DDoS Attacks Using Decision Trees

Alomin,

Gargouri,

Ali Ghorbel

2024

2024 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)

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A comprehensive survey on low-rate and high-rate DDoS defense approaches in SDN: taxonomy, research challenges, and opportunities

Cited by 4 publications

References 125 publications

A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges

A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Enhancing Network Security in SDN: Detecting Low-Rate DDoS Attacks Using Decision Trees

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