README.md

---
dataset_info:
  features:
  - name: srcip
    dtype: string
  - name: sport
    dtype: string
  - name: dstip
    dtype: string
  - name: dsport
    dtype: string
  - name: proto
    dtype: string
  - name: state
    dtype: string
  - name: dur
    dtype: float64
  - name: sbytes
    dtype: int64
  - name: dbytes
    dtype: int64
  - name: sttl
    dtype: int64
  - name: dttl
    dtype: int64
  - name: sloss
    dtype: int64
  - name: dloss
    dtype: int64
  - name: service
    dtype: string
  - name: Sload
    dtype: float64
  - name: Dload
    dtype: float64
  - name: Spkts
    dtype: int64
  - name: Dpkts
    dtype: int64
  - name: swin
    dtype: int64
  - name: dwin
    dtype: int64
  - name: stcpb
    dtype: int64
  - name: dtcpb
    dtype: int64
  - name: smeansz
    dtype: int64
  - name: dmeansz
    dtype: int64
  - name: trans_depth
    dtype: int64
  - name: res_bdy_len
    dtype: int64
  - name: Sjit
    dtype: float64
  - name: Djit
    dtype: float64
  - name: Stime
    dtype: int64
  - name: Ltime
    dtype: int64
  - name: Sintpkt
    dtype: float64
  - name: Dintpkt
    dtype: float64
  - name: tcprtt
    dtype: float64
  - name: synack
    dtype: float64
  - name: ackdat
    dtype: float64
  - name: is_sm_ips_ports
    dtype: int64
  - name: ct_state_ttl
    dtype: int64
  - name: ct_flw_http_mthd
    dtype: float64
  - name: is_ftp_login
    dtype: float64
  - name: ct_ftp_cmd
    dtype: string
  - name: ct_srv_src
    dtype: int64
  - name: ct_srv_dst
    dtype: int64
  - name: ct_dst_ltm
    dtype: int64
  - name: ct_src_ltm
    dtype: int64
  - name: ct_src_dport_ltm
    dtype: int64
  - name: ct_dst_sport_ltm
    dtype: int64
  - name: ct_dst_src_ltm
    dtype: int64
  - name: attack_cat
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 907689217
    num_examples: 2280090
  download_size: 230016344
  dataset_size: 907689217
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-classification
- zero-shot-classification
language:
- en
size_categories:
- 1M<n<10M
---
# The UNSW-NB15

The raw network packets (Pcap files) of the UNSW-NB 15 data set is created by the IXIA 
PerfectStorm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) 
for generating a hybrid of real modern normal activities and synthetic contemporary attack 
activities. The UNSW-NB15 source files are provided in different formats, Pcap files, BRO files, 
Argus Files and CSV files. The source files of the data set were divided based in the date of the 
simulation 22-1-2015 and 17-2-2015, respectively. The descriptions of these simulations are 
provided in the report files to show the network configurations and the actions of the attack types 
during the simulation

## How to Use it

 pip install datasets
 
 from datasets import load_dataset
 
 dataset = load_dataset("Mouwiya/UNSW-NB15")



### Dataset Description

The details of the UNSW-NB15 dataset were published in following the papers. For the academic/public use of this dataset, the authors have to cities the following papers:

Moustafa, Nour, and Jill Slay. "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)." Military Communications and Information Systems Conference (MilCIS), 2015. IEEE, 2015.
Moustafa, Nour, and Jill Slay. "The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 dataset and the comparison with the KDD99 dataset." Information Security Journal: A Global Perspective (2016): 1-14.
Moustafa, Nour, et al. "Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks." IEEE Transactions on Big Data (2017).
Moustafa, Nour, et al. "Big data analytics for intrusion detection system: statistical decision-making using finite dirichlet mixture models." Data Analytics and Decision Support for Cybersecurity. Springer, Cham, 2017. 127-156.
Sarhan, Mohanad, Siamak Layeghy, Nour Moustafa, and Marius Portmann. NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. In Big Data Technologies and Applications: 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings (p. 117). Springer Nature.


## Uses

<!-- Address questions around how the dataset is intended to be used. -->
Free use of the UNSW-NB15 dataset for academic research purposes is hereby granted in 
perpetuity. Use for commercial purposes is strictly prohibited. Nour Moustafa has asserted his
rights under the Copyright. 



## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
N. Moustafa and J. Slay, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia, 2015, pp. 1-6, doi: 10.1109/MilCIS.2015.7348942. keywords: {Telecommunication traffic;Feature extraction;Servers;Training;Data models;IP networks;Benchmark testing;UNSW-NB15 data set;NIDS;low footprint attacks;pcap files;testbed},