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Intrusion Detection research benchmarks – Page 5 · SOTA2 Research

Intrusion Detection

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

Deep Q-Network + QIM

32.5Latency (µs)

4

Jun 9, 2026

99.7Precision

4

Apr 7, 2026

99.89Accuracy

4

Mar 30, 2026

Attack Detection Dataset

0.96DDoS Detection Rate

4

Feb 26, 2026

10,000 sequences (526 vehicles) (inference)

0.003Data Setup Ratio

4

Feb 26, 2026

Encoder-Classifier

98.05Accuracy

4

Feb 26, 2026

97.86Accuracy

4

Feb 26, 2026

100Accuracy

4

Feb 26, 2026

Encoder-Classifier

99.99Accuracy

4

Feb 26, 2026

LID-DS CWE-434 2021

combined CWE detector

0.003FPR

3

Jun 23, 2026

LID-DS CWE-89 2021

combined CWE detector

0.1FPR

3

Jun 23, 2026

LID-DS CWE-307 2021

combined CWE detector

0.002False Positive Rate (FPR)

3

Jun 23, 2026

UNSW-NB 15 Full multi-class

Multi-stage optimized ML-based NIDS framework

99Accuracy

3

Jun 9, 2026

UNSW-NB 2015 (average)

Centralized Model (C)

23.45F1-macro

3

May 19, 2026

97.87Precision

3

May 19, 2026

100µAr

3

May 11, 2026

CICIDS binary setting 2017

99.989Accuracy

3

May 4, 2026

Song et al. (2020)

99.84Detection Rate (DR)

3

Apr 23, 2026

Cyberattack Dataset

100F1 Score

3

Feb 26, 2026

WUSTL EA Scenario

-3.86Backward Transfer

3

Feb 26, 2026

CICIDS EA Scenario 2018

-47Backward Transfer (%)

3

Feb 26, 2026

CICIDS EA Scenario 2017

5.99Backward Transfer

3

Feb 26, 2026

UNSW EA Scenario

4.14Backward Transfer

3

Feb 26, 2026

XIIoT EA Scenario

-0.49Backward Transfer

3

Feb 26, 2026

53.69Forward Transfer

3

Feb 26, 2026

Page 5 of 7

Popular datasets

CICIDS CIC-IDS N-BaIoT CICIoT UNSW-NB

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