This review paper discusses recent advances in detecting GNSS jamming and spoofing attacks. GNSS systems are vulnerable to various security threats, including jamming and spoofing, which can degrade navigation and positioning services. The availability of low-cost software-defined radios (SDRs) has made these attacks more accessible. Early detection of these attacks is crucial for mitigating their impact. The paper provides a comprehensive review of detection methods, categorizing them based on specific parameters and features. It highlights the increasing use of machine learning (ML) and deep learning (DL) methods for detecting GNSS jamming and spoofing. These methods include signal processing techniques, data bit methods, and machine learning models. The paper also discusses the use of antenna arrays, time of arrival (ToA), direction of arrival (DoA), and NMEA messages for spoofing detection. Additionally, it covers the use of radio frequency fingerprinting (RFF) and other methods for identifying spoofed signals. The paper concludes that ML methods, particularly SVM and KNN, have shown promising results in detecting spoofing attacks. The review also emphasizes the importance of using diverse datasets, such as TEXBAT and OAKBAT, for validating detection methods. Overall, the paper provides a detailed overview of current detection methods and highlights the need for further research to improve the accuracy and efficiency of GNSS jamming and spoofing detection.This review paper discusses recent advances in detecting GNSS jamming and spoofing attacks. GNSS systems are vulnerable to various security threats, including jamming and spoofing, which can degrade navigation and positioning services. The availability of low-cost software-defined radios (SDRs) has made these attacks more accessible. Early detection of these attacks is crucial for mitigating their impact. The paper provides a comprehensive review of detection methods, categorizing them based on specific parameters and features. It highlights the increasing use of machine learning (ML) and deep learning (DL) methods for detecting GNSS jamming and spoofing. These methods include signal processing techniques, data bit methods, and machine learning models. The paper also discusses the use of antenna arrays, time of arrival (ToA), direction of arrival (DoA), and NMEA messages for spoofing detection. Additionally, it covers the use of radio frequency fingerprinting (RFF) and other methods for identifying spoofed signals. The paper concludes that ML methods, particularly SVM and KNN, have shown promising results in detecting spoofing attacks. The review also emphasizes the importance of using diverse datasets, such as TEXBAT and OAKBAT, for validating detection methods. Overall, the paper provides a detailed overview of current detection methods and highlights the need for further research to improve the accuracy and efficiency of GNSS jamming and spoofing detection.