Payload-Byte

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Payload-Byte is a tool for extracting and labeling packet capture (Pcap) files of modern network intrusion detection datasets.

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# <img src="/Payload-Byte-logo.jpg" width="140" valign="middle" alt="Scapy" />&nbsp; Payload-Byte

[![DOI](https://zenodo.org/badge/524051176.svg)](https://zenodo.org/badge/latestdoi/524051176)

Payload-Byte is a tool for extracting and labeling packet capture (PCAP) files of modern network intrusion detection system datasets.

Since packet-based approaches for Network Intrusion Detection Systems (NIDS) suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Moreover, there are no standard labeled datasets available unlike flow-based datasets, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited from the proprietary interpretation of the dataset. Payload-Byte addresses the comparability and reproducibility issues by extracting and labeling network packets according to the available meta-data.

The function of this tool is to provide a standardized baseline for extracting and labeling PCAP files of available network intrusion detection system datasets for future reasearch work.

## Processed Datasets
For the ease of future researchers, we have included the processed and labeled payload data of two widely utilized network intrusion detection system datasets. They are available under `Data` Folder.

* UNSW-NB15
* CIC-IDS2017

## Usage

There are two different ways through which results can be generated completely.

1. The first one is using `Pipeline.ipynb` notebook which is a pipeline in which you just need to specify the directories of PCAP files and CSV files. Rest will be computed automatically. **Note:** For this approach, you should have enough space in your drive.  
2. The other approach is to utilize the functions seperately which is being illustrated in `Individual_approach` folder (UNDER CONSTRUCTION).

## Citation 
 If you are using our tool, kindly cite our related papers  [paper](https://www.techrxiv.org/articles/preprint/Payload-Byte_A_Tool_for_Extracting_and_Labeling_Packet_Capture_Files_of_Modern_Network_Intrusion_Detection_Datasets/20714221) which outlines the details of the tools and its processing. 
 
 ```yaml
@INPROCEEDINGS{Payload-Byte,
  author={Farrukh, Yasir Ali and Khan, Irfan and Wali, Syed and Bierbrauer, David and Pavlik, John A. and Bastian, Nathaniel D.},
  booktitle={2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)}, 
  title={Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets}, 
  year={2022},
  pages={58-67},
  doi={10.1109/BDCAT56447.2022.00015}}
```
Furthermore, our recent publication provides additional insights on how to effectively utilize payload information for classification.

[SeNet-I: An approach for detecting network intrusions through serialized network traffic images](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4370422))

```yaml
@article{SeNet-I,
title = {SeNet-I: An approach for detecting network intrusions through serialized network traffic images},
journal = {Engineering Applications of Artificial Intelligence},
volume = {126},
pages = {107169},
year = {2023},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2023.107169},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623013532},
author = {Yasir Ali Farrukh and Syed Wali and Irfan Khan and Nathaniel D. Bastian}}
```

[AIS-NIDS: An intelligent and self-sustaining network intrusion detection system](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4635437)

```yaml
@article{AIS-NIDS,
title = {AIS-NIDS: An intelligent and self-sustaining network intrusion detection system},
journal = {Computers & Security},
volume = {144},
pages = {103982},
year = {2024},
issn = {0167-4048},
doi = {https://doi.org/10.1016/j.cose.2024.103982},
url = {https://www.sciencedirect.com/science/article/pii/S0167404824002876},
author = {Yasir Ali Farrukh and Syed Wali and Irfan Khan and Nathaniel D. Bastian}}
```

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