Ryu Controller-based Attack Detection and Mitigation Method in Software Defined Internet of Things
Ryu Controller-based Attack Detection and Mitigation Method in Software Defined Internet of Things |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-9 |
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| Year of Publication : 2023 | ||
| Author : Pinkey Chauhan, Mithilesh Atulkar |
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| DOI : 10.14445/22315381/IJETT-V71I9P213 | ||
How to Cite?
Pinkey Chauhan, Mithilesh Atulkar, "Ryu Controller-based Attack Detection and Mitigation Method in Software Defined Internet of Things," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 138-156, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P213
Abstract
The innovative areas of software-defined networks and the Internet of Things are currently receiving significant attention in the IT industry and academic circles. As a result of their popularity, they have become a target for numerous attacks in the realm of SD-IoT (Software Defined Internet of Things). The attackers may aim to either pilfer or obstruct users’ data, in addition to depleting network resources through futility, thereby frustrating legitimate user demands. The category of attacks includes a form known as Distributed Denial of Service (DDoS). In this work, a centralized attack detection and mitigation approach has been proposed. For getting the most efficient attack detection and prevention method, a number of classifiers, namely Random Forest (RF), XGB, Light Gradient Boosting Machine (LGBM), ET, GB, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), NB, SVM(Linear), LR, and SVM(Poly) have been trained and tested on two controller-based datasets. Their performance has been evaluated under precision, F1, Cohen’s Kappa Coefficient (CKC), recall, accuracy, False Alarm Rate (FAR), Testing Time, and AUC value. In both datasets, it is discovered that LGBM outperforms all other classifiers, but here, the performance of LGBM on the second dataset is better than that of the first dataset, so finally, LGBM trained with the second dataset is deployed in the controller of SDN where it detects and mitigates the attack from the live traffic in SD-IoT.
Keywords
Distributed Denial of Service Attack, Ryu, Mininet, Software-defined Internet of Things, hping3, D-ITG.
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