Visualization of the adaptive learning process showing drift detection and model updating.

Visualization of the adaptive learning process showing drift detection and model updating.

<p>Visualization of the adaptive learning process showing drift detection and model updating.</p>

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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