An efficient centralized DDoS attack detection approach for Software Defined Internet of Things

Chauhan, Pinkey; Atulkar, Mithilesh

doi:10.1007/s11227-023-05072-y

Cited by 10 publications

(3 citation statements)

References 49 publications

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“…Cybercriminals can use behavioral data [48]. Consequently, businesses are required to attract more notice and be more proactive in protecting their data for several purposes [49]. The potential uses of these data include directing user experience models, providing services and products, helping businesses to make conscious decisions, and outfitting marketing methods [50].…”

Section: Various Features Of Iob For the Healthcare Sectionmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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Section: Various Features Of Iob For the Healthcare Sectionmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In Reference [35], the authors conducted a study on feature extraction techniques to identify DDoS attacks in SD‐IoT. 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).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Reference [35], the authors conducted a study on feature extraction techniques to identify DDoS attacks in SD-IoT. They evaluated their approach using the Distributed Internet Traffic Generator and hping3 tools and tested it with…”

Section: Introductionmentioning

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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A hybrid approach based on PUF and ML to protect MQTT based IoT system from DDoS attacks

Sharma,

Bhushan

2024

Cluster Comput

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An efficient centralized DDoS attack detection approach for Software Defined Internet of Things

Cited by 10 publications

References 49 publications

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

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

A hybrid approach based on PUF and ML to protect MQTT based IoT system from DDoS attacks

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