A SDN-based intelligent prediction approach to power traffic identification and monitoring for smart network access

Liu, Chuan; Zhang, Gang; Li, Bozhong; Ma, Rui; Jiang, Dingde; Zhao, Yong

doi:10.1007/s11276-019-02235-9

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…14 describes that the model will be programmed with consistent data, and all information would need to be converted to the training scale before it is fed into the model. The algorithm must return a weighted prediction and compare those predictions to achieve the performance metrics for comparison instances [35]. The classification report is a Scikit-Learn built-in metric developed especially for classification problems.…”

Section: Training and Testing Datasetmentioning

confidence: 99%

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

Shahzadi¹,

Ahmad²,

Basharat³

et al. 2021

Computers, Materials &Amp; Continua

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With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively. The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure. This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment. The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment, but as well as it provides the solution for critical problems specially regarding massive network traffic issues. The attacks have been expanding step by step; therefore, it is hard to recognize and protect by conventional methods. To overcome these issues, there must be an autonomous system to recognize and characterize the network traffic's abnormal conduct if there is any. Only four types of assaults, including HTTP Flood, UDP Flood, Smurf Flood, and SiDDoS Flood, are considered in the identified dataset, to optimize the stability of the SDN-NFV environment and security management, through several machine learning based characterization techniques like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Isolation Forest (IF). Python is used for simulation purposes, including several valuable utilities like the mine package, the open-source Python ML libraries Scikit-learn, NumPy, SciPy, Matplotlib. Few Flood assaults and Structured Query Language (SQL) injections anomalies are validated and effectively-identified through the anticipated procedure. The classification results are promising and show that overall accuracy lies between 87% to 95% for SVM, LR, KNN, and IF classifiers in the scrutiny of traffic, whether the network traffic is normal or anomalous in the SDN-NFV environment.

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Section: Training and Testing Datasetmentioning

confidence: 99%

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

Shahzadi¹,

Ahmad²,

Basharat³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Thus, the domain of area coverage and availability of information is limited to certain road area only, (ii) Vehicle data captured in the traditional system is hard to apply a data fusion approach. This is because tracing the target vehicle over the road is almost impossible [32,33]. In a drone-based system, it would be much easier to trace any target vehicle even if it moves off the road, (iii) The processing of data using drones and sharing with controller or cloud resources would be much efficient, faster, and secure compare to the traditional system, (iv) The drone-based moveable monitoring system can easily replace drones if it malfunctions without affecting the on-road traffic.…”

Section: Introductionmentioning

confidence: 99%

“…• In a SDN-integrated connected vehicular network, the possibility for increased speed and agility in the availability of network devices (both virtual and real) to users as a result of decreasing the requirement for human involvement [32]. Growing use of virtual private networks (VLANs) as a component of physical local area networks (LANs) has created a tangled web of dependencies and links between them.…”

Section: Introductionmentioning

confidence: 99%

A secure drone-to-drone communication and software defined drone network-enabled traffic monitoring system

Kumar

Yadav²,

Gill³

et al. 2022

Simulation Modelling Practice and Theory

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“…At present, there are few traffic situation prediction methods in SDN ( Hua et al, 2018 ). In Liu et al (2021) , a traffic situation prediction method based on RBFNN was proposed, which uses the SDN controller to collect network flow statistics, and uses RBFNN to analyze the nonlinear relationship of the network flow statistics to realize the traffic situation prediction. In Wang et al (2021) , a traffic situation prediction method based on ARMA was proposed.…”

Section: Introductionmentioning

confidence: 99%

A GRU-based traffic situation prediction method in multi-domain software defined network

Sun

Guan

2022

PeerJ Computer Science

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With the continuous development and improvement of Software-Defined Networking (SDN), large-scale networks are divided into multiple domains. Each domain, which is managed by a controller, forms multi-domain SDN architecture. In multi-domain SDN, the dynamics and complexity are more significant, bringing great challenges to network management. Comprehensively and accurately predicting traffic situation in multi-domain SDN can better maintain network stability. In this article, we propose a traffic situation prediction method based on the gated recurrent unit (GRU) network in multi-domain SDN. We first analyzed the relevant factors that affect data traffic and control traffic and transformed them into a time series of actual situation values. Then, to enhance the prediction performance of GRU, we used the salp swarm algorithm to optimize the hyperparameters of GRU automatically. Finally, we adopted hyperparameter optimized GRU to achieve traffic situation prediction in multi-domain SDN. The experimental results indicate that the proposed method outperforms other traditional machine learning algorithms in terms of prediction accuracy.

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A SDN-based intelligent prediction approach to power traffic identification and monitoring for smart network access

Cited by 8 publications

References 25 publications

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

A secure drone-to-drone communication and software defined drone network-enabled traffic monitoring system

A GRU-based traffic situation prediction method in multi-domain software defined network

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