A Framework for DDoS Attack Detection in SDN-Based IoT Using Hybrid Classifier

Chauhan, Pinkey; Atulkar, Mithilesh

doi:10.1007/978-981-19-5868-7_67

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…ISSN: 2302-9285 ❒ 641 [31], [32] Lorawan [33], [34], Wi-Fi [35], and many more, are found in this setting and are in a constant state of development [36]. − Application layer: it provides a means by which users can access data collected at the perception layer, modify those data to meet the needs of a given domain, and then feed those modified data into a processing network.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Software defined networking for internet of things: review, techniques, challenges, and future directions

Al-Shareeda,

Abdullah Alsadhan,

H. Qasim

et al. 2024

Bulletin EEI

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Security networks as one of the biggest issue for network managers with the exponential growth of devices connected to the internet. Keeping a big and diverse network running smoothly and securely is no easy feat. With this in mind, emerging technologies like software defined networking (SDN) and internet of things (IoT) hold considerable promise for information service innovation in the cloud and big data era. Therefore, this paper describes the model of SDN and the architecture of IoT. Then this review does not only review the research studies in SDN-IoT but also provides an explanation of the SDN-IoT solution in terms of architecture, main consideration, model, and the implementation of SDN controllers for IoT. Finally, this review discusses the challenges and future directions. This paper can be used as a starting point for thinking about how to improve SDN-IoT security and privacy.

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Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Software defined networking for internet of things: review, techniques, challenges, and future directions

Al-Shareeda,

Abdullah Alsadhan,

H. Qasim

et al. 2024

Bulletin EEI

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show abstract

“…To address this challenge, researchers have explored the use of machine learning algorithms for detecting DDoS assaults in SDIoT. 36 Machine learning (ML) is an artificial intelligence branch that develops models and algorithms to learn from data and make decisions or forecasts without explicit programming. In identifying DDoS threats, ML algorithms are utilized to examine vast volumes of real-time data produced by IoT devices and recognize patterns that could signify a DDoS attack.…”

Section: Machine Learningmentioning

confidence: 99%

“…They evaluated their approach using the Distributed Internet Traffic Generator and hping3 tools and tested it with different machine learning models such as Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), and K Nearest Neighbor (KNN). Similarly, in Reference [36], the same authors utilized the NSL‐KDD dataset to detect DDoS attacks in the SDIoT architecture. They employed a hybrid classifier stack that combined KNN, SVM, and Logistic Regression (LR) to identify the DDoS attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Detecting DDoS attacks in SDIoT is challenging due to the high volume and velocity of data generated by various IoT devices. To address this challenge, researchers have explored the use of machine learning algorithms for detecting DDoS assaults in SDIoT 36 . Machine learning (ML) is an artificial intelligence branch that develops models and algorithms to learn from data and make decisions or forecasts without explicit programming.…”

Section: Introductionmentioning

confidence: 99%

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An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

Setitra

Fan

Bensalem

2023

Trans Emerging Tel Tech

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The growing popularity of Software Defined Networks (SDN) and the Internet of Things (IoT) has led to the emergence of Software Defined Internet of Things (SDIoT) based on centralized network management by the Control Plane, which can handle the dynamic nature of IoT devices and the high volume of network traffic. However, due to their specific design, SDIoTs are the ideal target for Distributed Denial of Service (DDoS) attacks, becoming one of the most destructive threats. Machine learning (ML) techniques are best suited to solve this problem due to the recent growth and sophistication of DDoS attacks. In this study, we propose an enhanced deep learning approach based on combining AutoEncoder (AE) and Extreme Gradient Boosting (XGBoost). First, we applied the SHapley Additive exPlanations (SHAP) feature selection method to select the appropriate features subset according to their correlation results. Next, the AE is trained on the previous subset to learn a compact representation of the input features. The latent representation generated by the AE is then used as input for the XGBoost model, which is trained to predict the target variable and classify the traffic as usual or attack. In parallel, Grid Search Cross Validation (GSCV) is used to find the optimal hyperparameters for the AE‐XGBoost. The experimental results using two publicly available realistic SDN‐Iot datasets demonstrate that the proposed approach enables precise identification of DDoS attacks in SDIoT networks, achieving a 99.9920% accuracy, an F1 score of 0.999917, and a low false positive rate. Furthermore, the proposed model's performance exceeds that of the models used for comparison.

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“…Additionally, comprehensive evaluation under various attack scenarios and traffic conditions is lacking. [48] proposes a framework for DDoS attack detection in an SDN-based IoT environment using a hybrid classifier of decision trees and K-nearest neighbors (KNN). Evaluated on a simulated SDN-based IoT setup, the hybrid classifier demonstrates superior accuracy and false positive rates compared to other ML and deep learning techniques.…”

Section: Ddos Detection In Iot Networkmentioning

confidence: 99%

Sdn-Based Detection and Mitigation of Ddos Attacks on Smart Homes

Garba,

N Toosi,

Fermi Pasha

et al. 2023

Preprint

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A Framework for DDoS Attack Detection in SDN-Based IoT Using Hybrid Classifier

Cited by 4 publications

References 33 publications

Software defined networking for internet of things: review, techniques, challenges, and future directions

Software defined networking for internet of things: review, techniques, challenges, and future directions

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

Sdn-Based Detection and Mitigation of Ddos Attacks on Smart Homes

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