rates. AdversarialPSO is based on particle swarm optimization, and is flexible in balancing the number of queries submitted to the target compared to the quality of imperceptible adversarial examples. Finally, Devine and Bastian [@DevineBastian] used a machine learning approach for robust malware classification that integrates a MC simulation for adversarial perturbation with meta learning using a stacked ensemble-based methodology.

Methodology {#sec:Meth}
===========

In this section, we give a technical description of the features in the data sets used in our experiments. We then explain the details of the techniques employed for adversarial example generation. This leads to the layout of our computational setting.

Data sets
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There are a small number of publicly available, well-known labeled data sets of network traffic for cyber security research. Two of these data sets are: NSL-KDD [@10.5555/1736481.1736489] and UNSW-NB15 [@7348942]. Both data sets have a mix of benign and malicious traffic with a variety of network traffic types and attack types. Both data sets have quality limitations due to factors like generation methods, prevalence of attack traffic, and size. Nevertheless, these data sets are commonly used to evaluate machine learning based NIDS.

In general, the data sets contain fields with raw and processed data 