A new tool called Isolation Distributional Kernel (IDK) has been developed for detecting anomalies in data. Unlike previous methods, IDK uses a point kernel that is specific to the data being analyzed, making it more effective and efficient. IDK outperforms other existing anomaly detection methods without the need for explicit learning. This research shows that using a data-dependent point kernel is crucial for creating an effective anomaly detector based on kernel mean embedding.