UNSW-NB15 / README.md
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metadata
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

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

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},