Wireless indoor localization has become unavoidable for industrial indoor location-based services. Given the ubiquitous deployment of wireless access points (APs), Wi-Fi fingerprinting of Received Signal Strength (RSS) has been widely adopted for indoor localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities are exploited, mobile users may confront indoor localization mismatches, faults and even localization system failures. In this paper, we propose SE-Loc, a semi-supervised learning-based technique to enhance security and resiliency of fingerprint-based localization. The architecture of SE-Loc consists of two parts: (1) a correlation-based AP selection for processing RSS fingerprints and fingerprint-image generation, and (2) a deep learning model based on a denoising autoencoder and convolutional neural networks for robust feature learning and location matching. Extensive experiments show that under potential AP attacks, SE-Loc demonstrates superior performance on indoor localization over state-of-the-art methods. With up to 100 malicious attacking APs in the UJIIndoorLoc edge server, SE-Loc can still achieve the lowest error fluctuation of 1.7 m and the highest average localization accuracy of 8.9 m.