A Deep Learning Based Hybrid Structure for the Intrusion Detection

In the current digital landscape, in order to protect sensitive data and vital infrastructure from cyber assaults, computer network security is essential. Conventional IDS, which often rely on signature-based and rule-based techniques, face challenges in detecting novel and sophisticated attacks. This paper presents a new approach detect intrusion using a DL-based hybrid model that integrates CNNs, BiLSTM, BiGRU, and attention mechanisms. The proposed model leverages the strengths of these architectures to identify temporal and spatial patterns in the data of traffic of network Our hybrid model begins with a CNN component to extract high-level facets from raw network traffic data. These features are then processed by BiLSTM and BiGRU layers to capture patterns and temporal dependencies. The inclusion of bidirectional layers allows the model to learn from both past and future contexts, improving its ability to detect subtle and sophisticated attacks. Attention mechanism further refines the model’s pivot on pertinent characteristics of data, enhancing its detection capabilities. Various benchmark datasets commonly employed in the intrusion detection domain are used to evaluate the model. Obtained results demonstrate that the proposed model achieves an accuracy of approximately 98%, outperforming several advanced methods. The model’s robustness and generalizability to different network configurations and attack types are also validated. With its more precise and dependable approach for identifying and reducing cyber risks, this research advances intrusion detection systems. The results have important ramifications for the creation and application of cutting-edge security systems capable of proactively safeguarding critical infrastructure and confidential data in today’s interconnected world.