Treffer: Intelligent intrusion forecasting framework for distributed environment using federated learning.

Intrusion forecasting systems are developed to safeguard firm networks from attacks. Intrusion prediction is crucial for effectively recognizing and mitigating security breaches, protecting sensitive information, and ensuring integrity. Current approaches have limitations in intrusion detection (ID), such as distinguishing legitimate and strange events. This is because the existing techniques face challenges in analyzing large data, limitations in identifying the relevant features, improper noise filtering, and lack of adaptability to detect the attack patterns. Hence, to overcome a novel Puffer fish Federated Learning (PFFL) Framework for forecasting and classifying Intrusion is introduced. Initially, the datasets are collected, and noise is filtered by preprocessing. Subsequently, the relevant features are selected from the pre-processed data by Pufferfish optimization. Consequently, puffer fish fitness traces abnormal events, and they are predicted. Finally, the attack is classified. The Intrusion is classified as normal, DoS, probe, U2R, and R2L. The proposed PFFL model functions in the Python system. Eventually, the proposed PFFL framework was assessed using a few performance metrics and attained 99.52% accuracy, 99.51% Precision, 98.44% recall, 98.96% F score, 0.0047 error rate, and 624.86 s for computation time. The results demonstrate the proposed PFFL model's efficacy in ID and classification. [ABSTRACT FROM AUTHOR]