Abstract
In the quickly changing digital world of today, protecting oneself from cyberattacks is crucial. This study presents a novel method that uses TensorFlow Federated (TFF) Learning to merge BiLSTM and DNN architectures, improving the precision and effectiveness of intrusion detection systems (IDS). TFF offers a major paradigm change in model training by enabling decentralized learning on several servers or devices. TFF provides IDS with collective intelligence by fostering collaborative learning on remote data sources while protecting data privacy. This improves detection accuracy and strengthens defenses against adversarial attacks. By utilizing TensorFlow Federated methods, IDS may run DNN and BiLSTM models concurrently, maximizing processing speed and resource efficiency. The system's capacity to manage high-throughput data streams is ensured by this concurrent execution, which speeds up threat detection and response. Moreover, information sharing and smooth integration between concurrent processes are made possible via synchronization and communication protocols. The cooperative synergy between the various models improves IDS's dependability and efficacy in thwarting emerging cyberthreats.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Innovative Science and Research Technology (IJISRT)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
