This work established the domain and class-specific constraint levels, which an adversarial example must comply with to achieve realism on tabular data and introduced A2PM to fulfil these constraints in a gray-box setting, with only knowledge of the feature set. The capabilities of the proposed method were evaluated in a cybersecurity case study with two scenarios: Enterprise and IoT networks. MLP and RF classifiers were created with regular and adversarial training, using the network flows of the CIC-IDS2017 and IoT-23 datasets, and targeted and untargeted attacks were performed against them. For each scenario, the impact of the attacks was analyzed, and assessments of example realism and time consumption were performed. The modular architecture of A2PM enabled the creation of pattern sequences adapted to each type of cyber-attack, according to the concrete constraints of the utilized datasets. Both targeted and untargeted attacks successfully decreased the performance of all MLP and RF models, with significantly higher declines exhibited in the Enterprise scenario.