## 6 Conclusions and Future Work  
This work demonstrated the effectiveness of Transformer-based models for attack attribution in multi-adversary network environments. Unlike traditional detection tasks, attribution requires identifying the specific source of an attack, a challenge compounded by class imbalance and adversarial diversity. Our approach addresses this gap in the literature and establishes a new benchmark for robust and accurate attribution under these conditions. Experimental results show that the Transformer architecture significantly outperforms conventional models such as the MLP, achieving near-perfect accuracy and F1-scores across all classes, including those with limited representation. This performance is largely due to the Transformer's ability to model long-range dependencies and leverage contextual information, enabling effective generalization in highly imbalanced scenarios. Despite these promising results, this study has limitations. It relied on a single dataset derived from a CTF competition, which, while offering authentic adversarial behavior, may not have fully captured the complexity and obfuscation techniques present in real-world attacks. In particular, the dataset's temporal scope was limited to 2.17 days and segmented into 272 rounds, which may not have reflected long-term or persistent attack behaviors. Additionally, the controlled nature of the competition--where all participants were known and the attack surface was predefined--differed from open-world environments with unknown adversaries and more diverse threat vectors. The artificial segmentation into 5 min rounds may also have affected the model's ability to generalize to asynchronous or irregular attack patterns. Furthermore, the dataset contained only malicious alerts, precluding the evaluation of false positives or the model's ability to distinguish between benign and malicious behavior. Future work should validate the model using more diverse data sources, such as enterprise network logs or public threat intelligence feeds, and explore its robustness through sensitivity analyses involving synthetic noise or alternative datasets. We also plan to integrate benign traffic into the input sequences to better reflect real-world conditions and assess the model's performance in mixed-traffic environments. Another important limitation is the model's limited interpretability. In cybersecurity, where misattribution can have serious consequences, understanding the rationale behind each decision is critical. To address this, we plan to incorporate techniques for visualizing attention weights, identifying key alerts that influence attribution, and analyzing misclassifications to improve transparency and trust. Additionally, we aim to evaluate the model's resilience against evasion tactics, such as the injection of decoy alerts or the perturbation of alert sequences. Understanding how such manipulations affect attribution performance is essential for real-world deployment and will inform the development of more robust attribution mechanisms. We also recognize that our current sequences included only alerts associated with malicious activity. However, benign traffic may also form part of an attacker's behavioral footprint, especially in stealthy or long-term campaigns. Future work will explore the integration of non-malicious events into the input sequences to provide a more complete representation of adversarial behavior. In parallel, we plan to benchmark the model under different hardware configurations and deployment scenarios (e.g., batch vs. streaming inference) to assess its suitability for real-time operation. We also intend to explore lightweight architectural variants, such as distilled Transformers or sparse attention mechanisms, to reduce inference latency without compromising attribution performance. Finally, we acknowledge that the use of sinusoidal positional encodings may oversimplify the temporal dynamics of multi-step attacks, particularly in asynchronous environments where the timing between steps is irregular.