Enhancing Intrusion Detection Through Federated Learning With Enhanced Ghost_BiNet and Homomorphic Encryption

Intrusion detection is essential for safeguarding computer systems and networks against unauthorized access, malicious activities, and security breaches. Its application domains include network security, information security, and cybersecurity across various sectors such as finance, healthcare, government, and industry. Federated learning-based intrusion detection offers improved performance compared to conventional mechanisms by leveraging decentralized data sources, preserving data privacy, and enhancing model generalization through collaboration among multiple organizations. However, challenges faced by existing federated learning-based intrusion detection mechanisms include ensuring data privacy and security, mitigating communication overhead, and enhancing detection accuracy. In order to overcome these issues, this research article proposes a federated learning-based intrusion detection methodology that leverages Enhanced Ghost_BiNet, a novel deep learning model, to enhance the security of information sharing and detection accuracy. Federated learning, a privacy-preserving machine learning technique, is utilized to enable multiple entities to collaboratively train a global intrusion detection model without sharing sensitive data. The proposed system first trains local models using Enhanced Ghost_BiNet, which integrates GhostNet and Bidirectional Gated Recurrent Unit (BiGRU). To optimize the model’s performance, the Chaotic Chebyshev Artificial Humming Bird (CAh) algorithm is employed. Homomorphic encryption is applied to encrypt the local model updates, enhancing data privacy and security. Server-side aggregation of updates and collaborative optimization are introduced to minimize communication rounds during data aggregation. The results demonstrate that the Enhanced Ghost_BiNet outperforms traditional models like GhostNet, BiGRU, RNN, Auto Encoder, and CNN in terms of accuracy, precision, recall, F-Score, and mean square error (MSE). For instance, the Enhanced Ghost_BiNet achieves an accuracy of 99.24% on the KDD CUP 99 dataset, surpassing the other models by a significant margin. The proposed methodology provides a robust and secure approach to intrusion detection, ensuring the confidentiality of sensitive data while improving detection accuracy.