Fuzzy self organizing maps-based DDoS mitigation mechanism for software defined networking in cloud computing

Pillutla, Harikrishna; Amuthan, A.

doi:10.1007/s12652-018-0754-y

Cited by 50 publications

(16 citation statements)

References 27 publications

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“…In addition, Osanaiye et al [66] also examine existing research on DDoS attack and defense method in cloud computing and propose a reasonable cloud DDoS alleviation framework dependent on the change-point location. Pillutla and Arjunan [122] presents a Fuzzy self organizing maps-based DDoS mitigation (FSOMDM) technique that is ideally and suitably designed in cloud computing. Meanwhile, there are many research works [11], [26], [32], [34], [42], [58], [70], [71], [83], [91]- [93], [103], [106], [108], [111], [112], [121], [122] on moderating DDoS attack utilizing SDN.…”

Section: B Mitigating Ddos Attack In Cloud Computingmentioning

confidence: 99%

A Survey on Distributed Denial of Service (DDoS) Attacks in SDN and Cloud Computing Environments

2019

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Recently, software defined networks (SDNs) and cloud computing have been widely adopted by researchers and industry. However, widespread acceptance of these novel networking paradigms has been hampered by the security threats. Advances in the processing technologies have helped attackers in increasing the attacks too, for instance, the development of Denial of Service (DoS) attacks to distributed DoS (DDoS) attacks which are seldom identified by conventional firewalls. In this paper, we present the state of art of the DDoS attacks in SDN and cloud computing scenarios. Especially, we focus on the analysis of SDN and cloud computing architecture. Besides, we also overview the research works and open problems in identifying and tackling the DDoS attacks. INDEX TERMS Software defined network, cloud computing, distributed denial of service attacks (DDoS), DDoS attack and detection, experimental setup, survey.

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Section: B Mitigating Ddos Attack In Cloud Computingmentioning

confidence: 99%

A Survey on Distributed Denial of Service (DDoS) Attacks in SDN and Cloud Computing Environments

2019

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“…Parodied addresses must be obstructed for that in parodying the gadgets, clients of cloud conditions or customers on the web are mental fortitude to uncover its data and other touchy information. Current arrangements request Deep Packet Inspection (DPI) to identify and hinder the Packets instead of IP address to make framework harder for the programmers and DDoS assaults (18) .…”

Section: Related Workmentioning

confidence: 99%

Analyzing distributed denial of service attacks in cloud computing towards the Pakistan information technology industry

Memon¹,

Khuhro²,

Pirzada³

et al. 2020

IJST

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Objectives: Cloud computing technology is in continuous development, and with numerous challenges regarding security. In this context, one of the main concerns for cloud computing is represented by the trustworthiness of cloud services in the Pakistan Information Technology (IT) industry. This problem requires prompt resolution because IT organizations in Pakistan adopting cloud services would be exposed to increased expenditures while at a higher risk. A survey conducted by International Data Corporation (IDC) in August 2008 confirms that security is the major barrier for cloud users in Pakistan. Material/methods: We have proposed the mathematical-based model for an estimate of our user's requirements regarding our resource investment based on queuing theory to overcome these security issues in cloud technology. We are adopting a system and real-world analysis using data set experiments. Result: In this article, we proposed a solution to overcome the Distributed Denial of Service (DDoS) attacks in a cloud computing environment in the Pakistan IT services; because along with discussed above phenomena, we also used DST and FTA (Fault Tree Analysis) for mentioned attacks. Conclusion: Our proposed solution based on Dempsters combination rule is to analyzed mixture proof of different types of cloud attacks in IT industries in Pakistan.

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“…The experimental results showed that the combination of DNS responses inspection and ML techniques was able to achieve higher accuracy compared to standalone ML techniques. Pillutla and Arjunan [234] introduced, FSOMDM, a fuzzy self‐organising maps‐based DDoS mitigation approach for cloud‐based SDNs. FSOMDM enhances the NN approach by replacing the neurons of the traditional Kohonen NN model by updating fuzzy rules.…”

Section: Artificial Intelligence In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

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Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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Fuzzy self organizing maps-based DDoS mitigation mechanism for software defined networking in cloud computing

Cited by 50 publications

References 27 publications

A Survey on Distributed Denial of Service (DDoS) Attacks in SDN and Cloud Computing Environments

A Survey on Distributed Denial of Service (DDoS) Attacks in SDN and Cloud Computing Environments

Analyzing distributed denial of service attacks in cloud computing towards the Pakistan information technology industry

Artificial intelligence enabled software‐defined networking: a comprehensive overview

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