The Vehicular Reference Misbehavior Dataset (VeReMi) is a vital resource for advancing Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV). However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi's usability and relevance. Through 10 % down-sampling, the dataset was reduced to ∼724MB, making it computationally manageable. Biases were addressed by balancing benign and malicious samples through synthesis and identifying benign instances using predefined criteria. A refined feature set, including key attributes like rcvTime, pos_0, pos_1, and attack_type (renamed attacker_type), was selected to improve machine learning compatibility. This preprocessing pipeline effectively maintains data integrity and preserves the representativeness of malicious patterns. The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.

Bachelor of Science Indian Institute of Technology Madras BS Degree Office 3rd Floor ICSR Building IIT Madras Chennai 600036 India

Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 50003 Czech Republic

Department of Computer Science and Engineering Jadavpur University 188 Raja S C Mallick Rd Kolkata 700032 West Bengal India

Research Center Skoda Auto University Na Karmeli 1457 293 01 Mlada Boleslav Czech Republic

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