Depicts attacks on CIC-IDS2017.

<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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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Nasrullah Khan (13778350) (author)
مؤلفون آخرون:	Muhammad Ismail Mohmand (15138544) (author), Sadaqat ur Rehman (11820011) (author), Zia Ullah (9343911) (author), Zahid Khan (341388) (author), Wadii Boulila (18864345) (author)
منشور في:	2024
الموضوعات:	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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Depicts attacks on CIC-IDS2017.

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