# Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques

arXiv: https://arxiv.org/abs/1912.04549 · HTML: https://mlsanity.com/paper/1912.04549/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector](https://mlsanity.com/paper/2301.01044/) (2023)
- [Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning](https://mlsanity.com/paper/2101.00521/) (2021)
- [Privacy Protection of Grid Users Data with Blockchain and Adversarial Machine Learning](https://mlsanity.com/paper/2101.06308/) (2021)
- [ENCODE: Encoding NetFlows for Network Anomaly Detection](https://mlsanity.com/paper/2207.03890/) (2022)

## Abstract (arXiv)

Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model due to the imbalances in the dataset for discriminating attacks from the overall dataset. Multilayer Perceptron (MLP) technique will provide improvement in accuracy and increase the performance of detecting the attack and benign data from a balanced dataset. We have worked on the UGR'16 dataset publicly available for this work. Data wrangling has been done due to prepare test set from in the original set. We fed the neural network classifier larger input to the neural network in an increasing manner (i.e. 10000, 50000, 1 million) to see the distribution of features over the accuracy. We have implemented a GAN model that can produce samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan). We have been able to generate as many samples as necessary based on the data sample we have taken from the UGR'16. We have tested the accuracy of our model with the imbalance dataset initially and then with the increasing the attack samples and found improvement of classification performance for the latter.