## 5 Conclusions  
In this study, we explored the domain of trustworthiness of ML models and evaluated an approach for achieving robustness against adversarial evasion attacks with tree-based models in communication networks. ML models trained with GA-based adversarial examples were studied against adversarial attacks from the ART library--ZooAttack with XGBoostClassifier and ZooAttack with LightGBMClassifier. Our focus was on the network intrusion detection systems using two network communication-related datasets, CIC2019 and 5G-NIDD, and thus, we were able to contribute existing knowledge with results related to these specific use cases. We observe that the potential robustness [23; 24] coming with GA comes with a cost: the detection accuracy of ML models for intrusion detection is degraded in the context of the studied network cases. The continued exploration of adversarial training methods is essential to achieve robustness of generative models against attacks, ensuring they maintain accuracy under hostile conditions. Despite advancements, challenges remain, such as the inability of models to generalize across different datasets and the reliance on unrelated features for predictions. Addressing these limitations will be crucial for the development of trustworthy AI systems. In contrast, while focusing on robustness is vital, it is equally important to consider the trade-offs involved, such as potential increases in computational costs and complexity, which may hinder practical deployment in resource-constrained environments. As our study focused on the accuracy of models, future work should be carried out to analyze other costs, e.g., computational overheads, of GA-based adversarial training. Also, our focus was the accuracy of ML models and costs against them; the approach was optimized for the accuracy of intrusion detection. Future work is needed to explore the maximization of robustness, e.g., by considering robustness-driven fitness function criteria in GA.  
Further, future work is needed to understand impacts of genetic GAs in other use cases. A significant gap remains in establishing a standardized framework for certifying model robustness across domain specific scenarios; this is a major bottleneck to comparative analysis. The complex architectures inherent in deep learning models and other factors, such as data bias, present significant challenges in the identification of vulnerabilities, thereby necessitating more focused research on these issues. Conceptualization, A.T.; methodology, A.T.; software, A.T.; validation, A.T.; investigation, A.T.; writing--original draft preparation, A.T.; writing--review and editing, J.S. and A.T.; project administration, J.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the AI-NET-ANTILLAS project, partially funded by Business Finland. The work has also been supported by ERCIM. The datasets analyzed are as follows: Canadian Institute for Cybersecurity. (2019). Datasets Research IDS 2017. University of New Brunswick, [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) (accessed on 14 March 2024) and \"5 GTN\", [https://5 gtnr.fi/](https://5 gtnr.fi/) (accessed on 12 August 2024). In this section, the authors acknowledge technical support received from [PERSON], Research Team Leader and ERCIM Fellow Supervisor, Cybersecurity Engineering and Automation @VTT, Finland; [PERSON], Director, Data Analysis, Department of Computer Sciencewith the support from [PERSON], Connected Societies @RISE, Sweden as the host during the ERCIM Fellow visit to RISE, Sweden; and [PERSON] on insights on trustworthiness of ML. Authors [PERSON] and [PERSON] are employed by the company VTT Technical Research Centre of Finland.