The field of localization systems is moving towards more secure and accurate methods, with a focus on detecting and mitigating malicious attacks. Researchers are exploring the use of machine learning and deep learning techniques to improve the security and reliability of localization systems. One of the key challenges is the vulnerability of these systems to spoofing attacks, which can have severe consequences. To address this, researchers are proposing novel approaches such as self-supervised federated learning frameworks and extended receiver autonomous integrity monitoring frameworks. These approaches aim to improve the resilience of localization systems against location spoofing attacks and provide more accurate location data. Noteworthy papers include: Self-Supervised Federated GNSS Spoofing Detection with Opportunistic Data, which proposes a self-supervised federated learning framework for GNSS spoofing detection that outperforms position-based and deep learning-based methods. Securing WiFi Fingerprint-based Indoor Localization Systems from Malicious Access Points, which proposes a long-term reliable indoor localization scheme that detects malicious APs and mitigates their effects, achieving a detection accuracy above 95% for each attack type.