To address the issues of overfitting on new classes, limited generalization ability on old classes, and catastrophic forgetting of incremental learning-based incremental intrusion detection system (IDS) when dealing with attacks of new classes, an adversarial assistance-augmented incremental IDS is proposed. In the incremental training, the regularization of adversarial samples is leveraged to mitigate the overfitting on new classes. A dual distribution simulation buffer that stores both clean and adversarial samples of old classes is proposed to enhance the generalization ability to old classes. In addition, weighted cross-entropy loss is introduced into the training process to alleviate the catastrophic forgetting. Experimental results on the CSE-CIC-IDS2018 dataset and the UNSW-NB15 dataset show that direct participation of adversarial samples in training leads to deterioration of the recognition performance, while participation in the form of detached data distribution enhances the recognition performance of the model. The storage of adversarial samples in the buffer effectively suppresses the loss of the models generalization ability for old classes, and the adjustment of learning weights by weighted cross-entropy loss alleviates the catastrophic forgetting caused by the imbalance between the new classes and the data in the buffer. The proposed method offers a viable strategy for detecting real attacks within dynamic and complex networks, presenting substantial practical applicability.