## 5 Conclusions and Future Works  
In this paper, we, first, introduce five FDI attacks designed to stealthily manipulate the SoC values reported by consumers, increasing the likelihood of malicious consumers being selected for charging. Using real-world dataset, we run experiments to investigate the impact of these attacks on a RL-based charging coordination system. The experimental results confirm that these attacks significantly benefit the attackers, allowing them to raise their battery levels from 28% to 42% in one attack, while the battery levels of honest consumers drop from 28% to 18% as a result of the FDI attacks. Additionally, the attack degrades the performance of the charging coordination system, with the fairness metric dropping by 30% and the overall reward decreasing by 38%. We also observe that these negative effects worsen as the number of attackers increases. To counter these impacts, we develop a deep learning-based false SoC detector, and our experiments demonstrate that the detector is highly accurate in identifying FDI attacks (98.27%), with low false alarm rates (2.27%).  
In our future work, we will address several research challenges. This paper does not explore privacy concerns, which are crucial in practice. Consumers may be unwilling to share their SoC data as it could reveal sensitive personal information, such as their travel habits or sleep patterns. To tackle this, we plan to investigate the use of federated learning to train our RL-based charging coordination system and the SoC falsification detector without requiring direct access to consumer data. Additionally, we will examine adversarial attacks aimed at disrupting the training process by reducing model accuracy or introducing vulnerabilities that could enable evasion or Trojan attacks. Another potential extension of our work involves integrating the proposed approach with blockchain technology [29] and continuous authentication methods [30] to enhance security and data integrity. Incorporating blockchain could enable decentralized data management, providing tamper-proof transaction logs and secure communication between distributed components within the charging coordination system. Finally, in this paper, we used a supervised machine learning model to detect five types of attacks targeting an RL-based charging coordination model. In case that other attacks are launched, either the supervised machine learning model is retrained to detect the new attacks or unsupervised machine learning is used. We will investigate these two options in our future work.  
Conceptualization, A.A.E., I.E., A.T.E., M.M., W.E., M.A. and T.A.; Methodology, A.A.E., I.E., A.A.E., M.M., W.E., M.A. and T.A.; Validation, A.A.E., I.E., M.M. and M.A.; Investigation, A.A.E., I.E., A.T.E., M.M., W.E., M.A. and T.A.; Writing--original draft, A.A.E., I.E. and A.T.E.; Writing--review & editing, A.A.E., I.E., A.T.E., M.M., W.E., M.A. and T.A.; Supervision, M.M., W.E., M.A. and T.A. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** & **ROC-AUC (\%)** & **False Alarm Rate (\%)** \\ \hline Logistic Regression & 83.70 & 78.49 & 92.12 & 84.76 & 83.83 & 24.47 \\ \hline CNN & 98.27 & 97.69 & 98.83 & 98.26 & 98.28 & 2.27 \\ \hline CNN-GRU & 95.67 & 94.13 & 97.27 & 95.67 & 95.70 & 5.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance metrics for Logistic Regression, CNN, and CNN-GRU models.  
**Funding:** This work was supported by Researchers Supporting Project number (RSPD2024R636), King Saud University, Riyadh, Saudi Arabia and NSF grant #2349104.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All links to the data used in the study are cited in the text.  
**Conflicts of Interest:** The authors declare no conflict of interest.