Decoy Traffic-Aware IDS: A Novel Approach Using CSHHHNN and GGGOA

In the digital world, the Network Intrusion Detection System (NIDS) plays a prime role in protecting against cyber threats. Nevertheless, none of the prevailing studies focused on analyzing the impact of decoy traffic in networks, thereby resulting in poor security. To address this research gap, this article proposes a decoy traffic-aware NIDS that incorporates enhanced Cognitive Spiking Hyperbolic Hodgkin-Huxley Neural Networks (CSHHHNN) and the Greylag Griewank Goose Optimization Algorithm (GGGOA). The process starts with node initialization and registration, followed by network segmentation via clustering and subnet masking schemes. Thereafter, possible routes are recognized for each node, and then, a graph structure is generated. Next, multipath routing is established between the source and destination. Afterward, the network paths are verified. If a valid path is found, then it is selected; otherwise, it is discarded. Now, data sensing and data encryption are done. Thereafter, load balancing is performed in the encrypted data. Then, the load-balanced data is fed into the intrusion detection system. In the proposed NIDS, the dataset is initially pre-processed. Afterward, the relevant features are extracted. Subsequently, optimal features are selected, and privacy measures are applied to the model. Finally, the improved CSHHHNN model is used to classify the network traffic into normal or attack categories. The proposed approach demonstrates superior performance with 99.5124% accuracy in decoy traffic management and intrusion detection.