Authors

Cristopher Luis Jiménez M. (cristopher1 in GitHub) and Nicolás Esteban Matus P. (linusmaastok in GitHub)

Description

This repository contains a jupyter notebook (ann_with_unsw-db15.ipynb) created to train and tests ANNs to detect cyber attacks based on the paper "Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques" and the dataset UNSW-NB15.

• The folder datasets/unsw-nb15 contains the training.csv and testing.csv files that contains the data used to training and testing the ANNs
• The jupyter notebook will create a folder named "models", which is used to save the ANNs.

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References

1. 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.

2. 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.

3. 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).

4. 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.

5. 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.