Secure access of resources in software‐defined networks using dynamic access control list

Ramprasath, J.; Seethalakshmi, V.

doi:10.1002/dac.4607

Cited by 12 publications

(1 citation statement)

References 19 publications

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“…Ramprasath and Seethalakshmi [11] examines a crucial element of SDN, focusing on the requirement for improved security controls in SDN systems. By separating the information plane from the control plane, SDN enables ondemand services and the ability to configure networks dynamically.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering

Khekare,

Kumar,

Prasanthi

et al. 2023

IJACSA

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By offering flexible and adaptable infrastructures Software-Defined Networking (SDN) has emerged as a disruptive technology that has completely changed network provisioning and administration. By seamlessly integrating Hybrid Generative Adversarial Network-Recurrent Neural Network (GAN-RNN) modeling into the foundation of SDN-based traffic engineering and accessibility control methods, this work presents a novel and comprehensive method to improve network efficiency and security. The proposed Hybrid GAN-RNN models address two important aspects of network management: traffic optimization and access control. They combine the benefits of Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs). Traditional traffic engineering techniques frequently find it difficult to quickly adjust to situations that are changing quickly within today's dynamic networking environments. The models' capacity to generate synthetic traffic patterns that nearly perfectly replicate the complexity of real network traffic demonstrates the power of GANs. Network administrators can now allocate resources and routing methods more dynamically, as well as in responding to real-time network inconsistencies, due to this state-of-the-art technology. The technique known as Hybrid GAN-RNN addresses the enduring problem of network security. With their reputation for continuous learning and by utilizing Python software, recurrent neural networks (RNNs) are at the forefront of developing flexible management of access rules. With an incredible 99.4% accuracy rate, the "Proposed GAN-RNN" approach outperforms the other approaches. A comprehensive evaluation of network traffic and new safety risks allow for the immediate modification of these policies. This work is interesting because it combines hybrid GAN-RNN algorithms to strengthen security protocols with adaptive access control while also optimizing network efficiency through realistic traffic modeling.

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Section: Related Workmentioning

confidence: 99%

Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering

Khekare,

Kumar,

Prasanthi

et al. 2023

IJACSA

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DMoiSDN: A defensive mechanism of object integrity for SDN

Alsalamh,

Al‐Mutairi,

Humayed

et al. 2023

Concurrency and Computation

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SummarySoftware‐defined network (SDN) technology is widely used for computer networks, especially in enterprise data centers and virtualized networking. However, SDN networks encounter severe challenges to security. One such challenge comes from third‐party applications that contain malicious logic and security vulnerabilities, resulting in controller integrity attacks. In this paper, we propose a defensive mechanism of object integrity for SDN (DMoiSDN) to mitigate the issue known as Cross‐App Poisoning (CAP). Our results contribute to increasing the integrity level of the controller's resources by conducting a potential risk analysis, which showed a decrease of 57% in the risk factor for potential attacks. We further examined the results of comparing DMoiSDN's performance with related work that uses information flow control (IFC) policies. The best results among the three conducted scenarios were as follows: we found that decreased latency in our system ranged from 12% to 90%, with an average of 59%, when encountering an increase in requests. It ranged from 78% to 49%, with an average of 63%, when receiving a variable number of total permissions for each application. DMoiSDN is expected to show a perceptible but reasonable latency and, to some extent, be able to avoid a critical impact on performance.

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Detection of Parkinson’s Disease in Brain MRI Images Using Deep Learning Algorithms

Kalyan Chakravarthy,

Hima Bindu,

Jafar Ali Ibrahim

et al. 2023

Smart Innovation, Systems and Technologies

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Secure access of resources in software‐defined networks using dynamic access control list

Cited by 12 publications

References 19 publications

Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering

Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering

DMoiSDN: A defensive mechanism of object integrity for SDN

Detection of Parkinson’s Disease in Brain MRI Images Using Deep Learning Algorithms

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