introduction
This is an undergraduate course project in computer security.
The task is to fine tune the large model to achieve malicious network flow data detection.
base model
bert-base-uncased
dataset:
19kmunz/iot-23-preprocessed-minimumcolumns
example prompt:
18081 tcp S0 2 80 0
237215 tcp S0 2 80 0
352869 tcp S0 2 80 0
48080 tcp S0 2 80 0
580 tcp S0 2 80 0
The above are "malicious", which is "label_1".
167 udp S0 11 3608 0
20 icmp OTH 9 844 0
3136 icmp OTH 3 216 0
40 icmp OTH 8 648 0
5134 icmp OTH 2 96 0
The above are "Benign", which is "label_0".
accuracy
1 Training Loss Valid. Loss Valid. Accur.
2epoch
31 0.288545 0.190351 0.929988
42 0.147658 0.154426 0.943510
53 0.108059 0.173112 0.943510
64 0.092468 0.161035 0.947416
MCC score： 0.816
The "Total MCC" refers to the Matthews Correlation Coefficient (MCC), typically used to assess the quality of predictions in binary classification problems.
The MCC value ranges from -1 to 1, where 1 signifies perfect predictions, 0 indicates predictions similar to random chance, and -1 denotes completely opposite predictions.
A model with an MCC value of 0.816 can be considered quite good. This value being close to 1 implies that the model has a high predictive capability and can classify samples with considerable accuracy. A higher MCC value closer to 1 indicates stronger predictive ability in the model.
In summary, an MCC value of 0.816 indicates that the model demonstrates a high level of accuracy and predictive capability in binary classification tasks.
