FPR and FNR analysis based on varying cyber attack types.

FPR and FNR analysis based on varying cyber attack types.

<p>FPR and FNR analysis based on varying cyber attack types.</p>

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Bibliographic Details
Main Author:	Mohanad Faeq Ali (21354273) (author)
Other Authors:	Mohammed Shakir Mohmood (21354276) (author), Ban Salman Shukur (21354279) (author), Rex Bacarra (21354282) (author), Jamil Abedalrahim Jamil Alsayaydeh (21354285) (author), Masrullizam Mat Ibrahim (21354288) (author), Safarudin Gazali Herawan (19749996) (author)
Published:	2025
Subjects:	Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination outperformed existing models model &# 8217 maximum recognition rate including false positive implemented using python identifying abnormal patterns false positive rate false negative rates false negative rate causes prediction inaccuracies computational complexity issues communication networks within indicate potential cyberattacks iomt threat detection dynamic iomt ecosystem recurrent neural networks interconnected healthcare devices enhancing cyberattack prediction dataset availability issues computational efficiency enhances sensitive healthcare data recurrent networks healthcare devices computational efficiency detecting cyberattacks efficiency depends iomt infrastructure iomt environments xlink "> various metrics training data rfe ), results demonstrated research introduces research focuses rapid development raising concerns principal component optimization technique medical things learning techniques irrelevant features innovative machine gathered information extracted features evolving threats cybersecurity attacks created system attacks threaten
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