Distributed denial of service (DDoS) attacks pose a critical network security threat by exhausting server resources and bandwidth, rendering systems incapable of delivering essential services. While bidirectional long short-term memory (BLSTM) neural networks can detect these attacks, the bidirectional learning (BL) model, despite its suitability for handling large-scale and multi-attribute datasets, suffers from temporal interdependence limitations and suboptimal performance. In this paper, we introduced the cuckoo search (CS) bidirectional learning method (CSBLM), an innovative optimization model that enhanced the BL network performance through dynamic parameter tuning. At its core, CSBLM leveraged an optimized CS algorithm to fine-tune crucial BL neural network parameters, dynamically optimizing both the number of hidden units in the LSTM layer and the ideal time series length for processing. This sophisticated parameter optimization strategy represents a significant advancement in DDoS attack detection methodology. Experimental results demonstrated CSBLM’s superior performance compared with conventional optimization approaches, including gray wolf and particle swarm optimization models. The implementation of CSBLM achieved outstanding results, significantly reducing the number of network operation iterations while enhancing detection accuracy to an impressive 99.09%. These outcomes firmly established CSBLM as a powerful solution for improving DDoS attack detection, offering both enhanced efficiency and exceptional accuracy in identifying and mitigating network security threats.