Confusion matrix of RF.

<div><p>Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networ...

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Bibliographic Details
Main Author:	Nasrullah Khan (13778350) (author)
Other Authors:	Muhammad Ismail Mohmand (15138544) (author), Sadaqat ur Rehman (11820011) (author), Zia Ullah (9343911) (author), Zahid Khan (341388) (author), Wadii Boulila (18864345) (author)
Published:	2024
Subjects:	Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified pose significant threats lightweight hybrid rnn applying classification algorithms achieved accuracy scores 9 %, respectively network security laboratory safeguard data security intrusion detection methodologies data using rnns cic 2017 dataset data security intrusion detection 2017 datasets data preprocessing utilizing rnns traditional methods random forest proposed process pipeline underscores paper introduces numerous attacks nsl dataset major shift hidden layers greatly differs following steps feature extraction extracting features decision tree critical need conventional sequence catboost classifiers canadian institute approach provides
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Confusion matrix of RF.

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