Dataset characteristics comparison.

<div><p>The rapid evolution of cyber threats poses significant challenges to the adaptability and performance of anomaly detection systems. This study presents an innovative hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory network...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Jun Wang (5906) (author)
مؤلفون آخرون:	Ning Huang (623370) (author), Houzhong Zhang (22521907) (author), Luyun Liu (18972569) (author), Qiang Fu (129583) (author), Kerang Cao (21602045) (author), Xiwang Guo (22521910) (author), Hoekyung Jung (21602051) (author)
منشور في:	2025
الموضوعات:	Developmental Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified term memory networks representing significant improvements new attack patterns knowledge preservation strategy key contributions include 20 hours post network anomaly detection maintains detection capabilities anomaly detection systems temporal traffic patterns day simulated period learning model fusion adaptive learning mechanism proposed framework offers div >< p cicids2017 datasets respectively 4 ± 0 metric drift detection learning mechanism detection rate transformer model proposed cnn framework ’ day attacks anomalous traffic transformer models synergistic two study presents recent methods rapid evolution promising direction normal vs hybrid cnn handling dynamic experiments demonstrate evolving threats developing efficient detect drifts binary classification average accuracy autonomously responds
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Dataset characteristics comparison.

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