Authors : R Padmashani; Harshan R.; Logeshwaran C.; Srikrishna R.; Vijay Sundar

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/36xtn576

Scribd : https://tinyurl.com/k6kbjjh8

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN030

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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.

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