percentage of the data at under 25 percent of the total. This data set was also purposely constructed to more closely match the content of the training set and test set as highlighted in [@doi:10.1080/19393555.2015.1125974]. The data has entries with 49 fields of which some are generated by network flow analyzers and intrusion detection tools. Engineering these fields results in 196 features of which we identified 23 as mutable. Similar to the NSL-KDD data set, some fields are constrained as binary, increasing in value only, or have a linear relationship. However, some fields in this set can have decreasing values such as $sttl$ (e.g. the time to live can arbitrarily be set by the sender), and $sload$ (e.g. the source can increase/decrease packet sizes and transmission rates).

Adversarial Example Generation
------------------------------

Adversarial examples have been extensively used to evade machine learning systems. The methods of generation for these adversarial examples include mostly heuristic techniques [@foley_adversarial_2017; @sheatsley_adversarial_nodate; @yang_generative_2017] and GANs [@goodfellow_generative_nodate; @lin_idsgan:_2018; @hu2017generating]. In this work, we introduce two novel techniques for adversarial example generation that use evolutionary computation, PSO and GA, along with a GAN that uniquely considers constrained feature sets to avoid the failure of the resulting traffic. Finally, 