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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Veröffentlicht in:IEEE internet of things journal Jg. 10; H. 10; S. 8477 - 8490
Hauptverfasser: Oluchi Anyanwu, Goodness, Nwakanma, Cosmas Ifeanyi, Lee, Jae-Min, Kim, Dong-Seong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 15.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3199712