PrivGuardNet: A Federated Transformer-based Intrusion Detection System with Differential Privacy for Scalable Cybersecurity Defense

There are now more interconnected systems in today’s digital infrastructure which makes them more vulnerable to advanced cyber attacks. Conventional centralized IDSs usually face problems related to privacy of data, their ability to scale and speed. This paper suggests PrivGuardNet, a new Federated Learning-based Intrusion Detection System that relies on Transformer models and includes differential privacy to protect sensitive information among distributed nodes. With PrivGuardNet, the process of identifying anomalies is spread out, so that no personal data is shared. Transformer-based models which use self-attention, can detect intrusions by finding complex patterns in network traffic. Through the use of Differential Privacy, it is impossible for attackers to use model inversion or reconstruction attacks. Tests on important benchmark cybersecurity data reveal that PrivGuardNet outperforms both traditional and deep learning IDS models in terms of accuracy, generalization and resistance to privacy leaks.