Adversarial Optimization Scheme for Threat Detection Based on Hierarchical Oracle Supervision

Abstract

Conventional threat detection methodologies encounter substantial limitations in real-world complex network settings, owing to their dependence on single-dimensional analysis, vulnerability to adversarial perturbations, and propensity for overfitting. This study introduces an adversarial training optimization framework that incorporates hierarchical label encoding and prompt learning, designed to enhance model robustness and generalization in threat detection. The framework first establishes a hierarchical structure of attack scenarios and types, leveraging a graph attention network to encode semantic and structural dependencies among labels. Subsequently, the classification task is reformulated as a masked language modeling problem through prompt learning, enabling effective semantic alignment. Furthermore, a novel adversarial training mechanism is proposed, which utilizes local hierarchical information as a potent regularization signal. Within a game-theoretic architecture comprising a generator, an encoder, and a discriminator, the encoder is steered to integrate authentic hierarchical priors and produce high-fidelity oracle representations. This process encourages the generator to implicitly assimilate sample-specific hierarchical knowledge during adversarial learning, thereby improving the model's resilience to noisy inputs and its capacity to detect infrequent attacks. Evaluation results show that this method achieves an accuracy of 0.9972–1.0000 in complex mixed scenarios for scenario detection and 0.9987–0.9999 for attack category detection, while exhibiting strong robustness against interference.