Enhancing Network Security through Software Defined Networking (SDN)

Shin, Seungwon; Xu, Lei; Hong, Sae Yong; Gu, Guofei

doi:10.1109/icccn.2016.7568520

Cited by 104 publications

(64 citation statements)

References 27 publications

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“…In Shin et al, several examples of SDN‐based security applications are described, while the feasibility of deploying various security functions at the SDN control level has been investigated in Yoon et al Another survey focused on SDN's strengths and weaknesses against distributed denial‐of‐service (DDoS) attacks in cloud computing environments.…”

Section: Background On Sdn/nfv‐based Security Enablers For Iot Networkmentioning

confidence: 99%

“…For example, the network administrator can implement anomaly analysis to identify network-wide attacks by monitoring the network state changes, for example, to detect anomalous traffic peaks generated by distributed botnet of compromised IoT devices. In Shin et al, 11 several examples of SDN-based security applications are described, while the feasibility of deploying various security functions at the SDN control level has been investigated in Yoon et al 12 Another survey 13 focused on SDN's strengths and weaknesses against distributed denial-of-service (DDoS) attacks in cloud computing environments.…”

Section: Sdn Security Mechanismsmentioning

confidence: 99%

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Enhancing IoT security through network softwarization and virtual security appliances

et al. 2018

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Summary Billions of Internet of Things (IoT) devices are expected to populate our environments and provide novel pervasive services by interconnecting the physical and digital world. However, the increased connectivity of everyday objects can open manifold security vectors for cybercriminals to perform malicious attacks. These threats are even augmented by the resource constraints and heterogeneity of low‐cost IoT devices, which make current host‐based and static perimeter‐oriented defense mechanisms unsuitable for dynamic IoT environments. Accounting for all these considerations, we reckon that the novel softwarization capabilities of Telco network can fully leverage its privileged position to provide the desired levels of security. To this aim, the emerging software‐defined networking (SDN) and network function virtualization (NFV) paradigms can introduce new security enablers able to increase the level of IoT systems protection. In this paper, we design a novel policy‐based framework aiming to exploit SDN/NFV‐based security features, by efficiently coupling with existing IoT security approaches. A proof of concept test bed has been developed to assess the feasibility of the proposed architecture. The presented performance evaluation illustrates the benefits of adopting SDN security mechanisms in integrated IoT environments and provides interesting insights in the policy enforcement process to drive future research.

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Section: Background On Sdn/nfv‐based Security Enablers For Iot Networkmentioning

confidence: 99%

Section: Sdn Security Mechanismsmentioning

confidence: 99%

Enhancing IoT security through network softwarization and virtual security appliances

et al. 2018

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“…This was the major problem in transmitting the intrusion based data packets. Shin et al suggested improvement to the network security that was constructed on the data plane. The software platform was also named as the management plane that helps control over the whole network flow.…”

Section: Related Workmentioning

confidence: 99%

A meta‐heuristic Bayesian network classification for intrusion detection

Prasath

Perumal

2018

Int J Network Mgmt

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Software-defined networking (SDN) is an innovative network paradigm much in demand today in academics and industry. In this network, the SDN controller must be able to observe and examine traffic flow through the network systems. However, intrusion-based data packets affect the whole system is a major drawback. To overcome this issue, we propose a Novel Agent Program (NAP) framework for preventing switches from the external compromised attacks. A Meta-Heuristic Bayesian Network Classification (MHBNC) algorithm for intrusion detection is proposed in this paper. The proposed algorithm follows certain procedures for preprocessing, feature selection, feature optimization, and classification. Normal and anomaly-based data packets are classified successfully with its improved detection capabilities based on the optimization technique. The simulation results of the proposed ID_MBC (intrusion detection based on meta-heuristic Bayesian classifier) technique is compared with existing techniques such as the association rule, PSO+GA, and the GA+RVM. The proposed MHBNC classifier performs better than existing methods.Int J Network Mgmt. 2019;29:e2047.wileyonlinelibrary.com/journal/nem

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“…With the development and application of SDN, the deficiency of itself to deal with security threats has gradually emerged [1]. The data layer devices receive instructions from the controller through the southbound interface and complete specific network data processing according to these instructions.…”

Section: Introductionmentioning

confidence: 99%

TrustBlock: An adaptive trust evaluation of SDN network nodes based on double-layer blockchain

Zhao

Liu

et al. 2020

PLoS ONE

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The data layer devices in the Software Defined Network (SDN) play an important role in packet forwarding. However, whether the forwarding task can be efficiently completed by the node has not attracted enough attention. A method called TrustBlock is proposed in this paper, which introduces trust as a security attribute in SDN routing planning. Besides, in order to enhance the integrity and controllability of trust evaluation, the double-layer blockchain architecture is established. In the first layer, the behavior data of the node is recorded, and then the trust calculation is performed in the second layer. In the evaluation model, nodes' trust is calculated from three aspects: direct trust, indirect trust and historical trust. Firstly, from the perspective of security, blockchain is used to achieve identity authentication of nodes, after that, from the perspective of reliability, the forwarding status is used to calculate the trust value. Secondly, consensus algorithm is used to filter malicious recommendation trust value and prevent colluding attacks. Finally, the adaptive historical trust weight is designed to prevent the periodic attack. In this paper, the entropy method is used to determine the weight of each evaluation attribute, which can avoid the problem that the subjective judgment method is not adaptable to the weight setting. Simulation results show that the detection rate of the TrustBlock is up to 98.89%, which means this model can effectively identify the abnormal nodes in SDN. Moreover, it is attractive in terms of integrity and controllability.

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Enhancing Network Security through Software Defined Networking (SDN)

Cited by 104 publications

References 27 publications

Enhancing IoT security through network softwarization and virtual security appliances

Enhancing IoT security through network softwarization and virtual security appliances

A meta‐heuristic Bayesian network classification for intrusion detection

TrustBlock: An adaptive trust evaluation of SDN network nodes based on double-layer blockchain

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