Adversarial training defense (ATD) and virtual adversarial training (VAT) are the two most effective methods to improve model robustness against attacks and model generalization. While ATD is usually applied in robust machine learning, VAT is used in semi-supervised learning and domain adaption. In this paper, we introduce a novel adversarial local distribution regularization. The adversarial local distribution is defined by a set of all adversarial examples within a ball constraint given a natural input. We illustrate this regularization is a general form of previous methods (e.g., PGD, TRADES, VAT and VADA). We conduct comprehensive experiments on MNIST, SVHN and CIFAR10 to illustrate that our method outperforms well-known methods such as PGD, TRADES and ADT in robust machine learning, VAT in semi-supervised learning and VADA in domain adaption. Our implementation is on Github: https://github.com/PotatoThanh/ALD-Regularization.