LP-CRI: Label Propagation Immune Generation Algorithm Based on Clustering and Rebound Mechanism

Many existing immune detection algorithms rely on a large volume of labeled self-training samples, which are often difficult to obtain in practical scenarios, thus limiting the training of detection models. Furthermore, noise inherent in the samples can substantially degrade the detection accuracy of these algorithms. To overcome these challenges, we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation (LP-CRI). The dataset is randomly partitioned into multiple subsets, each of which undergoes clustering followed by label propagation and evaluation. The rebound mechanism assesses the model’s performance after propagation and determines whether to revert to its previous state, initiating a subsequent round of propagation to ensure stable and effective training. Experimental results demonstrate that the proposed method is both computationally efficient and easy to train, significantly enhancing detector performance and outperforming traditional immune detection algorithms.