DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions

Bawany, Narmeen Zakaria; Shamsi, Jawwad Ahmed; Salah, Khaled

doi:10.1007/s13369-017-2414-5

Cited by 304 publications

(107 citation statements)

References 65 publications

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“…By adapting SDN's programmability and its dynamic nature, adaptive distributed DoS (DDoS) protection mechanisms and intrusion detection systems can be implemented to improved SDN‐IoT structure. Thus, this helps in improving security aspects of IoT …”

Section: Software‐defined Networkmentioning

confidence: 99%

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Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Pohrmen

Das

Saha

2019

Trans Emerging Tel Tech

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Blockchain is a key technology that enables cryptocurrencies such as Bitcoin, Litecoin, etc. In recent years, researchers have ventured into tapping the potential of blockchain‐based ecosystems beyond cryptocurrencies. This paper explores the convergence of blockchain with the Internet of things (IoT) and its improved versions like software‐defined network (SDN)–based IoT, which are upcoming technologies in the area. This paper gives a brief description of the IoT and its challenges, especially the security aspects. It also discusses how blockchain concept can further resolve these challenges on the SDN‐based IoT platform. This paper first discussed the evolution of conventional IoT to the SDN‐based IoT, which can resolve many drawbacks of a conventional IoT system. Also, it is focused on how the concept of blockchain can be converged with SDN‐based IoT system to further improve its security aspects. Although the convergence of these different technologies has resulted into many advantages, many associated challenges still remain. This paper highlighted these challenges and opportunities to discover many other research issues that need to be resolved. The details of current literature on the same issue has also been highlighted in this paper.

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Section: Software‐defined Networkmentioning

confidence: 99%

“…Thus, this helps in improving security aspects of IoT. 10 SDN structure can be divided into three layers as shown in Figure 3. This is similar to the three-layered architecture of IoT.…”

Section: Software-defined Networkmentioning

confidence: 99%

Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Pohrmen

Das

Saha

2019

Trans Emerging Tel Tech

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“…Some of the proposed literatures related to our proposed model are reviewed in this section. Methods, practices and solution to the DDoS attack detection and mitigation on SDN [11] in this proposal author clarify solutions and findings for detection and mitigation. They propose and present a proactive framework which is SDN based defense mechanism.…”

Section: Artificial Neural Network (Ann) Based Ddos Attack Detection mentioning

confidence: 99%

Artificial Neural Network (ANN) Based DDoS Attack Detection Model on Software Defined Networking (SDN)

Pradeepa¹,

Pushpalatha²

2019

IJRTE

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Software Defined Networking (SDN) is a modern emerging technology in networking. The great advantage of this network is, decoupling of the carrier plane and the control plane as well as which provides centralized control. A Controller is the intelligent part of SDN. It offers several benefits such as network programmability, dynamic computing, and cost-effective, high bandwidth. However, SDN has many security issues. The DDoS attack on SDN is a significant issue, and various proposals have been proposed for the detection and prevention of attacks. The main objective of this proposal is to detect DDoS attacks with the help of SDN techniques. In this proposal, a deep learning based Artificial Neural Network (ANN) model is used to detect the DDoS attacks. This can reduce learning time as well as detection time. To evaluate our model we use different machine learning algorithms and deep learning algorithm with different optimizers to train the network traffic which is generated in Mininet emulator and evaluates the results by various metrics such as detection rate, accuracy score, and confusion matrix with classification report. The result shows less detection time (4Secs)with a high accuracy score of 92% in our proposed Artificial Neural Network (ANN) model.

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“…To mitigate these risks, the cloud providers adopted many methods and techniques [11], [12] that can be used to prevent, detect and respond to attacks against the cloud. But in the other hand, as cloud strengthens its defense, the attackers find more creative and sophisticated ways targeting the cloud by using or creating loopholes in the cloud defense system.…”

Section: Figure 2 a Typical Architecture Of Ddos/edos Attacksmentioning

confidence: 99%

Mitigating Economic Denial of Sustainability Attacks to Secure Cloud Computing Environments

Zekri

Kafhali

Hanini

et al. 2017

TMLAI

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In cloud computing environment where the infrastructure is shared by millions of users, attackers have the opportunity to ensure more damage to the compromised resources. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to customers. The Distributed Denialofservice (DDoS) attack is considered one of the largest threats to the Quality of Service (QoS) of cloud services which is used to deny access for legitimate users of an online service. However, Economic Denial of Sustainability (EDoS) attack is a special breed of DDoS attack that attack exploits auto scaling feature of cloud. The Cloud Service Provider (CSP) scales the architecture automatically to serve those requests for which cloud consumer is charged. A consumer expects a sustainable profit after hosting his application on cloud. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs by increasing the cost and decreasing the profit. Hence, in this paper we propose a novel mitigation system against the EDoS attacks. Our system consists of source Checking, Counting, and Turing Test. The obtained simulation results show that our system works efficiently to mitigate the EDoS attack in cloud environment.

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DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions

Cited by 304 publications

References 65 publications

Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Artificial Neural Network (ANN) Based DDoS Attack Detection Model on Software Defined Networking (SDN)

Mitigating Economic Denial of Sustainability Attacks to Secure Cloud Computing Environments

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