Enhancing network security using FLAT for classifying intrusion attacks

Concerns over the security of sensitive data handled by internet of things (IoT) devices are being raised by their rising presence in many facets of our everyday life.Network security becomes critical when smart homes and industrial settings are used.This research presents a novel method for intrusion detection in network communication that combines federated learning approaches with conventional machine learning and deep learning algorithms.Federated learning with adversarial training (FLAT), the suggested approach, mixes decentralised (post-FLAT) and centralised (pre-FLAT) methods in a novel way to improve security measures.Adversarial training is incorporated into the model to provide an extra line of defence against possible attacks.With roughly 92% accuracy, precision, recall, and F1-score for the man-in-the-middle attack dataset, the FLAT method performs competitively.Additionally, for the active wiretap attack dataset, FLAT exhibits a commendable accuracy (93%), precision (95%), recall (87%), and F1-score (91%).This research contributes to advancing security measures in IoT environments by introducing FLAT as a powerful tool for intrusion detection.