Learning-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture

Ravi, Nagarathna; Shalinie, S. Mercy

doi:10.1109/jiot.2020.2973176

Cited by 194 publications

(80 citation statements)

References 28 publications

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“…Ravi et al [29] proposed a novel semisupervised learning algorithm for DDoS attack detection. The researchers successfully detected DDoS attacks with an accuracy of 96.28%.…”

Section: Related Work In Iiot Securitymentioning

confidence: 99%

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

et al. 2020

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The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security. INDEX TERMS Artificial neural network, cybersecurity, industrial Internet of Things, random neural network, support vector machine.

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“…Ravi et al [29] proposed a novel semisupervised learning algorithm for DDoS attack detection. The researchers successfully detected DDoS attacks with an accuracy of 96.28%.…”

Section: Related Work In Iiot Securitymentioning

confidence: 99%

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

et al. 2020

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“…Ravi and Shalinie 10 have presented a new method that decreases the sinkhole attacks and prevents saturation issues. Therefore, the key role of this paper is as follows:…”

Section: F I G U R E 1 Sinkhole Attack In Iotmentioning

confidence: 99%

“…Ravi and Shalinie 10 have presented a new method that decreases the sinkhole attacks and prevents saturation issues. Therefore, the key role of this paper is as follows: The Semi supervised deep extreme learning machine (SDELM) model is used to provide IoT security and detect malicious attacks. A new reduction algorithm is presented, which is classified as approximate algorithms and has not been presented in previous studies. The newly proposed mechanism is tested in a real hardware network to evaluate its performance in practice. A set of experiments is executed using the UNB‐ISCX measure data, and the results are compared with the existing approaches. In Ouaddah et al, 11 the authors have presented an equitable access framework.…”

Section: Sinkhole Attacks and Detection Approachesmentioning

confidence: 99%

A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystem

Zaminkar¹,

Sarkohaki

Fotohi

2020

Int J Communication

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Summary Internet of Things (IoT) provides the possibility for milliards of devices throughout the world to communicate with each other, and data are collected autonomously. The big data generated by the devices should be managed securely. Due to security challenges, like malicious nodes, many approaches cannot respond to these concerns. In this paper, a robust hybrid method, including encryption, is used as an efficient approach for resolving the Low‐Power and Lossy Networks (RPL) protocol concerns so that the devices are connected securely. Therefore, the proposed Detection of sinkholes in RPL (DSH‐RPL) method for securing the RPL protocol comprises the four following phases: the first phase creates a reliable RPL. The second phase detects the sinkhole attack. The third phase quarantines the detected malicious node, and the fourth phase transmits data through encryption. The simulation results show that the DSH‐RPL reduces the false‐positive rate more than 18.2% and 23.1% and reduces the false‐negative rate more than 16.1% and 22.78% and it also increases the packet delivery rate more than 19.68% and 25.32% and increases the detection rate more than 26% and 31% compared to SecTrust‐RPL and IBOOS‐RPL.

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“…Ravi et. al [30] presented LEDEM security solution that detects the DDoS attack in IoT network via SDN-Cloud architecture. Experiments were conducted on the testbed and emulated topology, and the result has been compared with the state-of-art solutions.…”

Section: ) Data Planementioning

confidence: 99%

An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT)

2020

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Learning-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture

Cited by 194 publications

References 28 publications

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystem

An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT)

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