Learnable Unified Neighbourhood-based Anomaly Ranking (LUNAR) is one of the neural network-based models adopted in this study. It addresses the first limitation by introducing learnability into local OCC methods, providing the capacity to adapt to diverse data distributions and structures, making it more flexible and expressive in handling complex, nonlinear, and high-dimensional data. Compared to traditional local OCC methods, the learnability of LUNAR empowers it to exhibit enhanced robustness across various values of the number of nearest neighbours, k. Besides, a pre-defined percentage of samples are generated outside of the ground-truth class from the uniform distribution and subspace perturbation, which are then used to introduce supervision to the unsupervised task.