A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Imran,; Ghaffar, Zeba; Alshahrani, Abdullah; Fayaz, Muhammad; Alghamdi, Ahmed Mohammed; Gwak, Jeonghwan

doi:10.3390/electronics10080880

Cited by 69 publications

(25 citation statements)

References 191 publications

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“…Overall, users are able to employ general network services (e.g., multicast and routing) to acquire either organizational or individual objectives. Users are able to employ related APIs between applications and controller, followed by abstracting the network through leveraging network services without having knowledge of the application's particulars [13], [14]. The presence of new flow at a switch in the SDN allows the switch to determine the forwarding path by transmitting a routing request to the centralized controller.…”

Section: A Background 1) Software-defined-networkingmentioning

confidence: 99%

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Halman

Alenazi

2023

IEEE Access

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The healthcare sector deals with sensitive and significant data that must be protected against illegitimate users. Software-defined networks (SDNs) are widely used in healthcare systems to ensure efficient resource utilization, security, optimal network control, and management. Despite such advantages, SDNs suffer from a major issue posed by a wide range of cyberattacks, due to the sensitivity of patients' data. These attacks diminish the overall network performance, and can cause a network failure that might threaten human lives. Therefore, the main goal of our work is to propose a machine learning-based cyberattack detector (MCAD) for healthcare systems, by adapting a layer three (L3) learning switch application to collect normal and abnormal traffic, and then deploy MCAD on the Ryu controller. Our findings are beneficial for enhancing the security of healthcare applications by mitigating the impact of cyberattacks. This work covers the testing of MCAD using a wide spectrum of both ML algorithms and attacks, and provides a performance comparison for every pair of ML algorithms/attacks to illustrate the strengths and weaknesses of different algorithms against a specific attack. The MCAD shows impressive performance, achieving an F1-score of 0.9998 and of 0.9882 on normal and attack classes, respectively, which implies a high level of reliability. MCAD also achieved 5,709,692 samples per second on throughput, which reflects a high-performance real-time system with respect to complexity. Additionally, it showed a positive impact on the network KPIs by increasing the throughput by 609%, and decreasing delay and jitter by 77% and 23%, respectively, compared to attack results.

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Section: A Background 1) Software-defined-networkingmentioning

confidence: 99%

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Halman

Alenazi

2023

IEEE Access

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“…Although, multiple ML techniques are applied on different application areas [36], [37] and the enhanced versions are proposed [38] and many researchers concerned with hybrid or ensemble techniques [8]. Majority voting is one of the popular and widely used ensemble techniques.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Rizwan¹,

Khan

Iqbal

et al. 2021

IEEE Access

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Water is an essential source of life for every living thing, and drilling is the only source to gain water from underground. Different advanced technologies have been used to minimize the time factor and labor force. Along with technology to be used, some other factors are equally essential to be considered, like water level, the hardness level of the land, and the number of days spent on the whole process. The study proposed a weighted voting classifier based on Differential Evaluation (DE) to classify the regions with different soil colors and land layers. The weights are assigned to the candidate classifiers based on their performance for each class. For the assignment of the optimal weight, the DE optimization algorithm is used. Moreover, the study presents a chained multi-objective regression model to simultaneously predict the water level and total depth on different locations. The proposed work facilitates the drilling industry to increase the rate of penetration (ROP) by selecting the region with soft soil and land layer. The prediction of depth and water level allows the industry to estimate water levels in different areas at different depths. The dataset is provided by the research organization, which contains information of different drilling points. The results of the proposed weighted voting classifier are compared with the traditional machine learning models (kernel Naive Bayes, Gaussian SVM, Quadratic SVM, and Bilayered Neural network) and state of the art voting classifier in terms of precision, recall, and accuracy. Moreover, the proposed regression model is evaluated by well-known evaluation metrics, including Mean Absolute Error, Mean Square Error, and R2 score. Finally, the comparison verifies the effectiveness of the enhanced optimization-based classifier and multi-objective regressor.

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“…With the growing software defined networking (SDN) paradigm as a new approach to redesigning the network architecture, the control plane is decoupled from the data plane [5], [6]. The logically centralized controller preserves a global view of the whole network.…”

Section: Introductionmentioning

confidence: 99%

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Samaan

Jeiad

2023

IJ-AI

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Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.

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A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Cited by 69 publications

References 191 publications

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

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