An Empirical Evaluation of Autoencoding-Based Location Spoofing Detection

Location integrity is highly crucial in mobile communications. In this regard, the attack attempting to falsify the position of mobile agents (known as location spoofing) is critical, and thus, detecting such spoofing attacks should be a vital function in the mobile communication setting. With its importance, previous studies explored location spoofing attacks. Still, they mainly used classification techniques based on supervised learning, confining the detector's capability to detect known attack patterns. This study evaluates the feasibility of the autoencoder-based scheme that constructs a profile for legitimate data instances to be resilient to intelligent, previously unseen types of attacks (e.g., evading attacks). We examine three types of autoencoder models designed based on different structures and conduct extensive experiments to measure the performance of the autoencoder models with both standard and variation attacks, with a comparison study with conventional supervised learning-based classification techniques. Our experimental results show that the autoencoder models produce comparable or even better performance than supervised learners, which may be limited only to detecting known patterns.