Few-Shot Learning-Based Network Intrusion Detection through an Enhanced Parallelized Triplet Network

Table 6

The detection results for different attack types on the UNSW_NB15 dataset.

Type	CNN	ResNet	XGBoost	Proposed
Reconnaissance	77.70	50.7	98.5	95.1
Backdoor	98.08	55.1	97.9	99.65
DoS	93.30	91.1	96.8	97.3
Exploits	88.50	90.6	91.9	96.3
Analysis	97.90	98.80	93.55	99.40
Fuzzers	73.30	86.4	71.2	97.10
Generic	99.3	99.6	99.5	99.7
Worms	70.45	72.72	70.45	100
Shellcode	67.72	47.35	94.9	90.48