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Vehicular Ad-hoc Networks (VANETs) are a crucial component of Cooperative Intelligent Transportation Systems (C-ITS), enabling vehicles to communicate and share vital information to enhance road safety and efficiency. Basic Safety Messages (BSMs), periodically broadcast by vehicles to provide real-time kinematic data, form the foundation of numerous safety applications within VANETs. Ensuring the security of BSMs is paramount, as malicious entities can exploit vulnerabilities to launch attacks that could have catastrophic consequences. In this study, we provide a comprehensive analysis of BSM attacks and detection mechanisms in VANETs. We begin by outlining the system model, security requirements, and attacker models relevant to BSMs. Then, we categorize and describe a range of attacks, from simple position falsification to more sophisticated and evasive techniques, such as the SixPack attack. We also classify existing attack detection methods into machine learning-based, deep learning-based, plausibility and consistency-based, and software-defined networking (SDN)-based mechanisms, analyzing their effectiveness and limitations. Additionally, we highlight the challenges in securing BSMs, such as the trade-off between model accuracy and real-time performance. Future research directions are also discussed. This survey paper serves as a foundational step towards building safe, secure, and reliable cooperative intelligent transportation systems and their associated applications.