A survey of link flooding attacks in software defined network ecosystems

Rasool, Raihan Ur; Wang, Hua; Ashraf, Usman; Ahmed, Khandakar; Anwar, Zahid; Rafique, Wajid

doi:10.1016/j.jnca.2020.102803

Cited by 23 publications

(12 citation statements)

References 162 publications

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“…Recently, machine learning and deep learning approaches have been widely used in network security for optimization and classification due to the rapid growth in cloud computing and big data [2]. For instance, Lal et al [9] introduced a framework that can guarantee the security and robustness of those artificial intelligence approaches by preserving the classification with correct labeling.…”

Section: Related Workmentioning

confidence: 99%

“…Various network attacks had caused severe damage to the Internet community in the real-world over recent years [1][2][3][4][5][6]. The link-flooding attack is a kind of distributed denial of service (DDoS) attack that does not directly attack the target devices but congests the target link with a large number of low-speed flows to reduce the connectivity between end devices, so it is difficult to detect with the intrusion detection system or firewall.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it draws the attention of designing an effective way to detect link-flooding attacks quickly to protect the Internet infrastructure from danger. Rasool et al [2] gave a pretty nice survey on this issue, and they suggested a possible solution via the machine learning approach. We propose a deep learning method to help resolve the link-flooding problem based on software-defined networking.…”

Section: Introductionmentioning

confidence: 99%

“…Link flooding attack is one of the most troublesome attacks in modern Internet society [2]. It utilizes a large quantity of seemingly normal traffic to congest the target link in the network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Detection of Link-Flooding Attacks with Deep Learning

Hsieh

Wang

et al. 2021

Sustainability

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The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and implement our ideas with a deep learning approach to improve the performance of the previous work. Through rerouting techniques and monitoring network traffic, our system can detect a malicious attack from the adversary. A CNN architecture is combined to assist in finding an appropriate rerouting path that can shorten the reaction time for detecting DDoS attacks. Therefore, the proposed method can efficiently distinguish the difference between benign traffic and malicious traffic and prevent attackers from carrying out link-flooding attacks through bots.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Detection of Link-Flooding Attacks with Deep Learning

Hsieh

Wang

et al. 2021

Sustainability

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“…Buts not focussed to optimize the number of controllers to be placed. In [28], the authors addressed the Link Flooding Attacks (LFA) in wired and wireless SDN by identifying its attack types and variants. They categorised and compared the existing mitigation on LFA on SDN ecosystems.…”

Section: Sdn Traffic Managementmentioning

confidence: 99%

Prediction Based Dynamic Controller Placement in SDN

Ramya¹,

Manoharan²

2018

ICST Transactions on Scalable Information Systems

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The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and control planes from the network devices and enables programmability of network devices. The major challenge in SDN is identification of number of controllers to be placed and its optimal placements in the network. To address this issue, this work proposes a Traffic Engineering mechanism that leverages the performance of Machine Learning to predict controller numbers by analysing and predicting the controller's traffic. The optimal locations of controllers are identified by using the K-Means++ algorithm. The proposed method is simulated using Mininet and the results depict that the proposed methodology outperforms the existing methodologies in terms of performance parameters.

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Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

Self Cite

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Smart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent-aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision-making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

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A survey of link flooding attacks in software defined network ecosystems

Cited by 23 publications

References 162 publications

Efficient Detection of Link-Flooding Attacks with Deep Learning

Efficient Detection of Link-Flooding Attacks with Deep Learning

Prediction Based Dynamic Controller Placement in SDN

Developing smart city services using intent‐aware recommendation systems: A survey

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