We present a novel kernel-based anomaly detection algorithm for model-independent new physics searches. The model is based on a re-weighted version of kernel logistic regression and it aims at learning the likelihood ratio test statistics from simulated anomaly-free background data and experimental data. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the data. This is made possible by kernel methods being non-parametric models that, given enough data, can approximate any continuous function and adapt to potentially any type of anomaly. This model shows dramatic advantages compared to similar neural network implementations in terms of training times and computational resources, while showing comparable performances. We test the model on datasets of different dimensionalities showing that modern implementations of kernel methods are competitive options for large scale problems.