This paper reviews recent advances in the detection of jamming and spoofing attacks in Global Navigation Satellite Systems (GNSS). The increasing integration of GNSS into various receivers has made them vulnerable to security threats such as jamming and spoofing. The availability of low-cost devices, including software-defined radios (SDRs), has made these attacks more accessible. Early detection is crucial for mitigating service degradation. The paper categorizes and discusses various detection methods, focusing on recent advancements. Machine learning (ML) methods, particularly classification and regression decision trees, are highlighted as the most effective for detecting and classifying GNSS spoofing attacks. Other methods include signal processing techniques, data bit methods, and machine and deep learning approaches. The paper also discusses the challenges and limitations of current detection methods and suggests future research directions. The review aims to provide a comprehensive reference for selecting appropriate detection solutions based on specific requirements and constraints.This paper reviews recent advances in the detection of jamming and spoofing attacks in Global Navigation Satellite Systems (GNSS). The increasing integration of GNSS into various receivers has made them vulnerable to security threats such as jamming and spoofing. The availability of low-cost devices, including software-defined radios (SDRs), has made these attacks more accessible. Early detection is crucial for mitigating service degradation. The paper categorizes and discusses various detection methods, focusing on recent advancements. Machine learning (ML) methods, particularly classification and regression decision trees, are highlighted as the most effective for detecting and classifying GNSS spoofing attacks. Other methods include signal processing techniques, data bit methods, and machine and deep learning approaches. The paper also discusses the challenges and limitations of current detection methods and suggests future research directions. The review aims to provide a comprehensive reference for selecting appropriate detection solutions based on specific requirements and constraints.