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

Table 5

Detection results for different attack types on the CICIDS-2017 dataset.

Type	CNN	ResNet	XGBoost	Proposed
FTP-Patator	99.80	88.17	81.50	99.50
SSH-Patator	97.90	98.63	98.30	98.70
DoS Hulk	95.10	96.87	99.70	95.50
DoS GoldenEye	94.90	79.30	98.90	95.20
DoS Slowloris	95.80	97.30	99.00	96.10
DoS Slowhttptest	93.90	95.10	96.80	97.90
Web Attack-Brute Force	99.60	96.90	97.40	99.70
Web Attack-XSS	94.23	93.30	99.07	97.35
Bot	88.10	97.66	92.30	97.70
DDoS	93.40	92.20	99.60	91.30
Port Scan	97.60	82.40	83.60	91.20
Heartbleed	18.18	100.00	27.27	100.00
Infiltration	11.11	8.00	8.33	47.22
Web Attack-Sql Injection	80.95	57.14	85.71	90.48

The values in bold represent the optimal values for the different methods in the comparison experiments.