Strategic Network Attack Prevention System Leveraging Sophisticated Query-Based Network Attention Algorithm (QNAA) and Self-Perpetuating Generative Adversarial Network (SPF-GAN) Techniques for Optimal Detection

Network attack detection is a critical issue in complex networks at present, one which becomes even more challenging as the network complexity grows and new threats emerge. Existing security models may encounter problems such as low accuracy, a high number of false positives, and the inability to learn new attacks, especially jamming attacks, where the attacker floods a communication channel with noise. Hence, an adaptive and resilient approach is required. This study presents two novel approaches—the Query-Based Network Attention Algorithm (QNAA) and the Self-Perpetuating Generative Adversarial Network (SPF-GAN) —to enhance performance and flexibility. The QNAA integrates attention mechanisms that allow the model to focus on features and patterns connected with attacks, while the SPF-GAN applies generative adversarial networks to mimic attack scenarios, improving the model’s predictive capability and robustness. The assessment outcomes indicate that the formulated model yields a higher accuracy, precision, recall, and F1-scores than conventional methods in identifying jammer attacks on different datasets.