An intelligent intrusion prediction and prevention system for software defined internet of things cloud networks

Kranthi, S.; Kanchana, M.; Suneetha, M.

doi:10.1007/s12083-022-01374-9

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The RYU controller assumes a pivotal role within the SDN architecture, serving as a crucial element in the separation of the control plane from the data plane. This segregation facilitates the implementation of dynamic and customizable network configurations [28,30]. RYU primarily oversees the management of network flows, effectively executing this task by leveraging protocols like OpenFlow to interface with SDN-enabled switches and routers.…”

Section: Ryu Controllermentioning

confidence: 99%

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Securing SDN Networks: Employing LSTM and Linear SVM Models for Enhanced Network Security with VPN Integration

Baji,

Suryadevara,

Bodapati

2024

AJCT

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In the ever-changing landscape of network security, Software-Defined Networking (SDN) is a critical foundation that requires strong protections against cyber threats. This research describes a novel strategy to fortifying SDN networks by combining Deep Learning (DL) and Machine Learning (ML) approaches. Our solution detects, analyses, and prevents potential security breaches in real time by using Long Short-Term Memory (LSTM) and Linear Support Vector Machine (SVM) models, as well as PyVPN integration. Our technology intends to improve SDN network resilience against a variety of cyber threats, including malware, intrusions, and denial-of-service attacks, by analysing network traffic patterns comprehensively and proactively identifying anomalies. Through extensive validation, we demonstrate the usefulness of our strategy in strengthening SDN network security, providinga robust defense mechanism against the everpersistent threat landscape.

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Section: Ryu Controllermentioning

confidence: 99%

“…Its dynamic network control, centralized management, and security enhancements were critical in efficiently and securely achieving our project objectives. For a more indepth exploration of how the OpenFlow protocol can be applied in projects similar to ours, we recommend referring to seminal works on the subject [30].…”

Section: C) Enhanced Security Measuresmentioning

confidence: 99%

Securing SDN Networks: Employing LSTM and Linear SVM Models for Enhanced Network Security with VPN Integration

Baji,

Suryadevara,

Bodapati

2024

AJCT

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“…In Reference [44], the authors investigated the efficacy of Moving Target Defense (MTD) based on SDN for countering DDoS attacks in IoT-Edge networks in a test scenario based on intelligent buildings. Meanwhile, in Reference [45], they developed a chimp-based AE Network (CbAN) for the SDIoT cloud network.…”

Section: Limitations and Research Gapmentioning

confidence: 99%

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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Wagging-Based Whale Optimization Algorithm to Enhance the Prediction of Intrusions in IoT Network

Thangarasu,

Alla

2024

Evolutionary Artificial Intelligence

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An intelligent intrusion prediction and prevention system for software defined internet of things cloud networks

Cited by 5 publications

References 30 publications

Securing SDN Networks: Employing LSTM and Linear SVM Models for Enhanced Network Security with VPN Integration

Securing SDN Networks: Employing LSTM and Linear SVM Models for Enhanced Network Security with VPN Integration

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

Wagging-Based Whale Optimization Algorithm to Enhance the Prediction of Intrusions in IoT Network

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