Optimization of RBF-SVM Kernel Using Grid Search Algorithm for DDoS Attack Detection in SDN-Based VANET

The dynamic nature of the vehicular space exposes it to distributed malicious attacks irrespective of the integration of enabling technologies. The software-defined network (SDN) represents one of these enabling technologies, providing an integrated improvement over the traditional vehicular ad-hoc...

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Vydáno v:	IEEE internet of things journal Ročník 10; číslo 10; s. 8477 - 8490
Hlavní autoři:	Oluchi Anyanwu, Goodness, Nwakanma, Cosmas Ifeanyi, Lee, Jae-Min, Kim, Dong-Seong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 15.05.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial intelligence Computer architecture Computer crime Cybersecurity Denial of service attacks Denial-of-service attack Distributed Denial-of-Service (DDoS) attack grid search cross-validation (GSCV) hyperparameter optimization Intrusion detection systems Kernel Kernels Mobile ad hoc networks Optimization Parameters Performance measurement Radial basis function radial basis function (RBF) kernel Reliability Search algorithms software-defined network (SDN)-based vehicular ad-hoc network (VANET) Software-defined networking support vector machine (SVM) Support vector machines Vehicular ad hoc networks
ISSN:	2327-4662, 2327-4662
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Popis
Shrnutí:	The dynamic nature of the vehicular space exposes it to distributed malicious attacks irrespective of the integration of enabling technologies. The software-defined network (SDN) represents one of these enabling technologies, providing an integrated improvement over the traditional vehicular ad-hoc network (VANET). Due to the centralized characteristics of SDN, they are vulnerable to attacks that may result in life-threatening situations. Securing SDN-based VANETs is vital and requires incorporating artificial intelligence (AI) techniques. Hence, this work proposed an intrusion detection model (IDM) to identify Distributed Denial-of-Service (DDoS) attacks in the vehicular space. The proposed solution employs the radial basis function (RBF) kernel of the support vector machine (SVM) classifier and an exhaustive parameter search technique called grid search cross-validation (GSCV). In this framework, the proposed architecture can be deployed on the onboard units (OBUs) of each vehicle, which receive the vehicular data and run intrusion detection tasks to classify a message sequence as a DDoS attack or benign. The performance of the proposed algorithm compared to other ML algorithms using key performance metrics. The proposed framework is validated through experimental simulations to demonstrate its effectiveness in detecting DDoS intrusion. Using the GridSearchCV, optimal values of the RBF-SVM kernel parameters "C" and "gamma" <inline-formula> <tex-math notation="LaTeX">(\gamma) </tex-math></inline-formula> of 100 and 0.1, respectively, gave the optimal performance. The proposed scheme showed an overall accuracy of 99.33%, a detection rate of 99.22%, and an average squared error of 0.007, outperforming existing benchmarks.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	2327-4662 2327-4662
DOI:	10.1109/JIOT.2022.3199712