
## Source
https://www.kaggle.com/datasets/dhoogla/unswnb15?resource=download

## Dataset

This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.

Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.

The full dataset also offers the pcap, BRO and Argus files along with additional documentation.

The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.

Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC. 
task_categories:tabular-classification size_categories:1M<n<10M tabular network region:us
{"size_categories": ["1M<n<10M"], "task_categories": ["tabular-classification"], "tags": ["tabular", "network"]}
TAGS
#task_categories-tabular-classification #size_categories-1M<n<10M #tabular #network #region-us 


## Source
URL

## Dataset

This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.

Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.

The full dataset also offers the pcap, BRO and Argus files along with additional documentation.

The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.

Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC. 
## Source
URL ## Dataset

This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.

Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.

The full dataset also offers the pcap, BRO and Argus files along with additional documentation.

The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.

Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC.
TAGS
#task_categories-tabular-classification #size_categories-1M<n<10M #tabular #network #region-us 
 ## Source
URL ## Dataset

This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.

Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.

The full dataset also offers the pcap, BRO and Argus files along with additional documentation.

The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.

Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC.
passage: TAGS
#task_categories-tabular-classification #size_categories-1M<n<10M #tabular #network #region-us 
## Source
URL## Dataset

This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.

Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.

The full dataset also offers the pcap, BRO and Argus files along with additional documentation.

The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.

Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC.