Intrusion Detection on CAR HACKING dataset

99.99Precision

LSTM-CNN

Updated 4d ago

Evaluation Results

Method
LSTM-CNN 2025.12	99.99	99.51	99.23	99.61
LSTM-CNN 2025.12	99.98	99.48	98.28	99.12
LSTM-CNN 2025.12	99.98	99.26	98.38	99.17
LSTM-CNN 2025.12	99.91	99.57	99.1	99.5
GAN 2025.12	98.3	98	90	-
GAN 2025.12	98.1	96.2	96.5	-
TCN (autoencoder) 2025.12	97.88	-	97.84	97.86
GAN 2025.12	97.3	98	99.5	-
GAN 2025.12	96.8	97.9	99.6	-
TCN (autoencoder) 2025.12	96.69	-	95.62	96.08
TCN (autoencoder) 2025.12	96.41	-	96	96.17
TCN (autoencoder) 2025.12	95.25	-	95.33	95.27
Transformer 2025.12	1	1	1	1
Transformer 2025.12	1	1	1	1
Transformer 2025.12	1	0.9999	1	1
VGG-16 2025.12	1	1	1	1
VGG-16 2025.12	1	0.96	0.96	0.98
VGG-16 2025.12	1	1	1	1
Transformer 2025.12	1	-	1	1
Transformer 2025.12	0.9999	-	0.9998	0.9999
Graph Convolutional Network 2025.12	0.9996	0.998	0.892	0.9427
Transformer 2025.12	0.9993	-	1	0.9996
Transformer 2025.12	0.9984	-	0.9971	0.9977
CNN-LSTM 2025.12	0.9958	0.9957	0.9957	0.9958
Transformer 2025.12	0.9955	-	0.9645	0.996
Graph Convolutional Network 2025.12	0.9953	0.998	0.9721	0.9836
Transformer 2025.12	0.9936	0.9947	0.9941	0.9976
Graph Convolutional Network 2025.12	0.9932	0.998	0.9355	0.9635
LSTM (autoencoder) 2025.12	0.99	0.99	1	0.99
FedLiTeCAN 2025.12	0.99	0.9992	1	1
FedLiTeCAN 2025.12	0.99	0.9926	0.93	0.96
FedLiTeCAN 2025.12	0.99	0.9992	1	1
FedLiTeCAN 2025.12	0.99	0.999	1	0.99
FedLiTeCAN 2025.12	0.97	0.9991	1	0.99
VGG-16 2025.12	0.94	0.99	0.99	0.9
Graph Convolutional Network 2025.12	0.7804	0.997	0.8392	0.8088
FFT-CNN 2025.12	-	0.999	-	0.997
FFT-CNN 2025.12	-	0.976	-	0.981
FFT-CNN 2025.12	-	0.992	-	0.989
FFT-CNN 2025.12	-	0.994	-	0.991