Cogaugids: a cognitive model-based data augmentation framework for intrusion detection under extremely small sample conditions

Abstract In current research on network intrusion detection systems (IDS), mainstream methods typically rely on large-scale, high-quality labeled datasets to train deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. These methods can achieve high detection accuracy and robustness under conditions where sufficient training data is available. However, during actual deployment, especially at the initial emergence of novel attacks or in specific scenarios, it is often difficult to collect sufficient and reliable labeled samples, leading to extremely small-sample conditions. Under extremely small-sample conditions, existing deep learning-based IDS methods experience significant degradation in both detection performance and generalization capability due to scarce training data or insufficient labeling quality. To address this problem, this paper proposes CogAugIDS, a cognitive model data augmentation-based IDS framework. CogAugIDS simulates human learning and decision-making processes to deeply understand and reason about extremely small-sample data, thereby generating more representative and diverse