Intrusion detection method based on imbalanced learning classification
Xiangjun Li, Ke Kong, Hua Shen, Zhixiang Wei, Xiaofeng Liao · Journal of Experimental & Theoretical Artificial Intelligence · 2022
Unbalanced data in intrusion detection seriously affect the overall performance of intrusion detection methods. This paper proposes an intrusion detection algorithm based on unbalanced learning (ID-UL) for unbalanced learning. It uses two strategies to address the issues brought about by unbalanced data. First, the data grouping strategy based on integrated learning alleviates the extreme data imbalance and avoids the negative impact on imbalanced learning. Meanwhile, to avoid the possible negative impact brought by unbalanced data on the learning procedures of convolutional neural networks, the strategy of assigning weight to the loss function is adopted. Besides, a method for determining the weight value is designed to make the model supervise the learning of attack samples during the training process. Experimental results on the NSL-KDD dataset show that the method in this paper can effectively improve the detection of attack classes, and the overall detection accuracy reaches 81.48%. ID-UL has the same significant effect on other imbalanced datasets.