Open-set recognition (OSR) is crucial for classifying known classes while identifying unknown data in real-world applications. Existing methods often rely on discriminative features from deep models but struggle with recognizing new data with non-discriminative differences from known classes and are susceptible to adversarial attacks. To address this limitation, we redefine the OSR problem by incorporating the unknown space with non-discriminative differences. Specifically, we introduce global feature representations that encapsulate both discriminative and non-discriminative features. Unlike discriminative features alone, global representations capture variations between arbitrary samples, thereby covering unknown spaces that deviate from known samples in various feature aspects. As an exploratory solution, we propose the Open Calibrated K-Nearest Neighbor (OpenCKNN) classifier based on global feature representation. OpenKNN, an open version of the traditional closed KNN, preserves all global features without information loss, enabling the recognition of all unknown classes. Moreover, KNN’s local neighbor learning and average nearest neighbor discrimination effectively handle challenges posed by irregular data distributions and atypical points. We also employ nearest-neighbor distance calibration using a pseudo-extreme value machine to mitigate inconsistencies across different class clusters. Extensive experiments on benchmark vision and intrusion detection datasets demonstrate that our approach significantly enhances open-set recognition and resistance to adversarial attacks.