Comparison of neural network paradigms for their usefulness in a real-world network intrusion detection deployment and a proposed optimised approach

Cybersecurity and security of IoT are vital to the proliferation of the technology. Yet, the number of cyberattacks is on the rise. One of the recent advancements in the fight against cyberthreats are machine-learning based network intrusion detection systems. This paper compares three state of the art methods with a proposed, optimised approach to showcase higher metrics and better execution times.

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