---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-unformatted-network-data-test-6-types
  results: []
widget:
  - text: "local ip address 59074 foreign ip address 53 17 20825 2 2 90.0 122.0 45.0 45.0 45.0 0.0 61.0 61.0 61.0 0.0 10180.072028811524 192.07683073229293 6941.666666666666 12021.587305066389 20823.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0 0 0 0 40 40 96.03841536614652 96.03841536614652 45.0 61.0 51.4 8.763560920082657 76.8 0 0 0 0 0 0 0 0 1.0 64.25 45.0 61.0 40 0 0 0 0 0 0 2 90 2 122 -1 -1 1 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0"
    example_title: "1 benign"
  - text: "foreign ip address 80 local ip address 60548 6 94 1 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31914.89361702128 47.0 63.639610306789294 92.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 2.0 0 0 0 0 32 64 10638.297872340429 21276.595744680853 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 1 1 0 2.0 0.0 0.0 0.0 32 0 0 0 0 0 0 1 0 2 0 243 245 0 32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "2 benign outside"
  - text: "foreign ip address 34386 local ip address 28303 17 116473 4 0 1458 0 384 345 364.5 22.516660498395403 0 0 0.0 0.0 12517.922608673256 34.34272320623664 38824.33333333333 67243.98585251572 116471 1 116473 38824.33333333333 67243.98585251572 116471 1 0 0.0 0.0 0 0 0 0 0 0 -4 0 34.34272320623664 0.0 345 384 360.6 21.36117974270148 456.3 0 0 0 0 0 0 0 0 0 450.75 364.5 0.0 -4 0 0 0 0 0 0 4 1458 0 0 -1 -1 3 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1"
    example_title: "3 udplag"
  - text: "foreign ip address 54265 local ip address 31612 17 1 2 0 802 0 401 401 401.0 0.0 0 0 0.0 0.0 802000000.0 2000000.0 1.0 0.0 1 1 1 1.0 0.0 1 1 0 0.0 0.0 0 0 0 0 0 0 -2 0 2000000.0 0.0 401 401 401.0 0.0 0.0 0 0 0 0 0 0 0 0 0 601.5 401.0 0.0 -2 0 0 0 0 0 0 2 802 0 0 -1 -1 1 -1 0.0 0.0 0 0 0.0 0.0 0 0 0 1"
    example_title: "4 udplag outside"
  - text: "foreign ip address 61614 local ip address 57728 6 1 2 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2000000.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 40 0 2000000.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 0 0 0 0.0 0.0 0.0 0.0 40 0 0 0 0 0 0 2 0 0 0 5840 -1 0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "5 syn"
  - text: "foreign ip address 62313 local ip address 26468 6 0 2 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan inf 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 40 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 0 0 0 0.0 0.0 0.0 0.0 40 0 0 0 0 0 0 2 0 0 0 5840 -1 0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "6 syn outside"
  - text: "foreign ip address 34183 local ip address 2276 17 1 2 0 884.0 0.0 442.0 442.0 442.0 0.0 0.0 0.0 0.0 0.0 884000000.0 2000000.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 40 0 2000000.0 0.0 442.0 442.0 442.0 0.0 0.0 0 0 0 0 0 0 0 0 0.0 663.0 442.0 0.0 40 0 0 0 0 0 0 2 884 0 0 -1 -1 1 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "7 netbios"
  - text: "foreign ip address 36952 local ip address 22857 17 1 2 0 912.0 0.0 456.0 456.0 456.0 0.0 0.0 0.0 0.0 0.0 912000000.0 2000000.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 -2125437950 0 2000000.0 0.0 456.0 456.0 456.0 0.0 0.0 0 0 0 0 0 0 0 0 0.0 684.0 456.0 0.0 -2125437950 0 0 0 0 0 0 2 912 0 0 -1 -1 1 -1062718975 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "8 netbios outside"
  - text: "foreign ip address 729 local ip address 56042 17 1 2 0 2944.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0.0 0.0 0.0 2944000000.0 2000000.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 -2 0 2000000.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0 0 0 0 0 0 0 0 0.0 2208.0 1472.0 0.0 -2 0 0 0 0 0 0 2 2944 0 0 -1 -1 1 -1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "9 mssql"
  - text: "foreign ip address 796 local ip address 26821 17 2 2 0 2944.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0.0 0.0 0.0 1472000000.0 1000000.0 2.0 0.0 2.0 2.0 2.0 2.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 64 0 1000000.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0 0 0 0 0 0 0 0 0.0 2208.0 1472.0 0.0 64 0 0 0 0 0 0 2 2944 0 0 -1 -1 1 32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "10 mssql outside"
  - text: "foreign ip address 900 local ip address 31091 17 1 2 0 2944.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0.0 0.0 0.0 2944000000.0 2000000.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 -2 0 2000000.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0 0 0 0 0 0 0 0 0.0 2208.0 1472.0 0.0 -2 0 0 0 0 0 0 2 2944 0 0 -1 -1 1 -1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "11 ldap"
  - text: "foreign ip address 900 local ip address 11399 17 1 2 0 2944.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0.0 0.0 0.0 2944000000.0 2000000.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 28 0 2000000.0 0.0 1472.0 1472.0 1472.0 0.0 0.0 0 0 0 0 0 0 0 0 0.0 2208.0 1472.0 0.0 28 0 0 0 0 0 0 2 2944 0 0 -1 -1 1 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 1"
    example_title: "12 ldap outside"
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-unformatted-network-data-test-6-types

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1318
- F1: 0.9624

# EXAMPLE FULL NAMES:

label_0 = UDP-lag DDoS, label_1 = benign, label_2 = SYN flood, label_3 = NetBIOS, label_4 = MSSQL, label_5 = LDAP

1. Benign traffic from training data
2. Benign traffic from outside training data
3. malicious UDP-Lag DDoS attack from training data
4. malicious UDP-Lag DDoS attack from outside of training data
6. malicious SYN flood attack from training data
7. malicious SYN flood attack from outside of training data
8. malicious NetBIOS DDoS attack from training data
9. malicious NetBIOS DDoS attack from outside of training data
10. malicious MSSQL DDoS attack from training data
11. malicious MSSQL DDoS attack from outside of training data
12. malicious LDAP DDoS attack from training data
13. malicious LDAP DDoS attack from outside of training data

examples from CIC-DDoS2019 (formatted for model training)
https://colab.research.google.com/drive/1PmLep9D3NfMhYsX0soTBhfVXFkawGgGx?authuser=0#scrollTo=ReaH6NCljdsn

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1427        | 1.0   | 2250  | 0.1279          | 0.9622 |
| 0.1348        | 2.0   | 4500  | 0.1517          | 0.9624 |
| 0.1331        | 3.0   | 6750  | 0.1467          | 0.9613 |
| 0.1407        | 4.0   | 9000  | 0.1294          | 0.9623 |
| 0.1229        | 5.0   | 11250 | 0.1318          | 0.9624 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1