ABSTRACT
In terms of service quality for transportation, security, robotics, healthcare, power, finance, etc., machine learning (ML) is effectively disrupting and modernizing cities. Even with their overwhelming success, machine learning (ML) algorithms still require significant computational work with fast computer hardware in order to handle model complexity and guarantees that result in effective, dependable, and robust solutions. Regarding cybersecurity and digital protection, quantum computing (QC) is a formidable contender to assist machine learning (ML) systems achieve optimal performance. The goal of this research is to provide the quantum Adaptive Neuro-fuzzy Inference System (QANFIS) model for smart control application distributed denial of service attack detection. The usefulness of our proposed model is demonstrated by evaluating it against an actual dataset of denial of service attack incidents. Lastly, various unresolved problems and difficulties with fitting ML with QC are discussed along with the findings.
