Impact of Deep Learning on Real-Time Network Intrusion Detection: A Quantitative Evaluation Using Benchmark Datasets
Maishar Mostari Zenia · 2025
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
This study quantitatively evaluated the effectiveness of deep-learning architectures for real-time network intrusion detection using heterogeneous benchmark datasets, with particular emphasis on predictive accuracy, attack-specific…
Excerpt shown for reference under fair use — read the full paper at the publisher.
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.63125/tnaksd40