Norouzi M., Gürkaş Aydın G. Z.
CLUSTER COMPUTING, cilt.29, sa.2, ss.110, 2026 (SCI-Expanded, Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
29
Sayı:
2
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Basım Tarihi:
2026
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Doi Numarası:
10.1007/s10586-025-05929-2
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Dergi Adı:
CLUSTER COMPUTING
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Derginin Tarandığı İndeksler:
Scopus, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC
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Sayfa Sayıları:
ss.110
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İstanbul Üniversitesi-Cerrahpaşa Adresli:
Evet
Özet
Abstract
Today, the Internet of Vehicles (IoV) is a new and very attractive technology in automotive communications. IoV networks can combine Internet-connected devices to store, process, and analyze real-time data in intelligent transportation. However, detecting cyberattacks in this environment is inevitable and remains a major challenge, as malicious threats can disrupt vehicle communications, leading to network congestion and safety risks. To enhance security in IoV networks, software-defined networking (SDN) provides a centralized and flexible framework for managing traffic flow and implementing security measures. In this study, we propose a novel Intrusion Detection System (IDS) for SDN-enabled IoV environments. Our proposed Genetic Algorithm-Ensemble Bagging Trees (GA-EBT) hybrid model employs the Message Queuing Telemetry Transport (MQTT) protocol for secure data transmission and integrates a hybrid machine-learning model to predict and detect cyber threats in IoV networks. Using the IoT_SDN-IDS and MQTT-IoT-IDS2020 datasets, we complete a comprehensive case study to evaluate various machine learning algorithms. Our findings indicate that our hybrid GA-EBT model performs more effectively than previous models. Simulation results show accuracy up to 99.9931% and 99.997% on the IoT_SDN-IDS and MQTT-IoT-IDS2020 datasets, respectively. The results prove that our hybrid SDN-based cyber-attack detection model effectively detects cyber-attack threats in IoV environments. Moreover, the proposed GA-EBT model provides secure data interaction and improves vehicular communication reliability.