Hybrid quantum enhanced federated learning for cyber attack detection.

Cyber-attack brings significant threat and become a critical issue in the digital world network security. The conventional procedures developed to detects are centralized and often struggles with concerns like data privacy and communication overheads. Due to this, conventional methods are unable to adapt quickly for different threats. This research aims to develop a novel solution to address these limitations through Federated Learning. The centralized approach is developed by integrating spatio-temporal attention network and also introduces a quantum inspired federated averaging optimization procedure for cyber-attack detection. The presented model utilizes a hierarchical model aggregation procedure which dynamically groups nodes into regions based on the network condition and data similarity. A robust global model is generated at the central server by aggregating intermediate models which are developed using weighted local models. Additionally, a multi-stage model refinement procedure and privacy preservation techniques are incorporated to improve overall security and performance. The novel STAN used in the proposed work captures the spatio-temporal patterns in the network traffic data. The optimization model QIFA utilizes quantum principles to enhance the federated learning procedure. Experimentation of the proposed model utilizes benchmark UNSW-NB15 dataset and evaluated the proposed model performances. The proposed model attained better performance in detecting different types of anomalies. With maximum precision of 98.2%, recall of 98.5%, f1-score of 98.35%, specificity of 98.2% and accuracy of 98.34%, the proposed model performs better than traditional CNN, LSTM, RNN and federated learning models.