# 5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
## 6 Conclusions  
In summary, the advent of 5G-based smart healthcare networks presents unparalleled opportunities for healthcare's transformation. However, these advancements also bring forth significant security challenges that require immediate attention. Notably, fake base station attacks pose a critical threat, compromising communication security, user privacy, and sensitive healthcare data. This paper delved into the multifaceted security concerns within 5G-based smart healthcare networks, with a specific focus on fake base station attacks. It underscored the importance of implementing robust security measures, such as private networks and blockchain-based solutions. Additionally, we introduced a detection mechanism for identifying suspicious base stations and discussed potential avenues for future research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**MCC** & **MNC** & **TAC** & **PhyID** & **EARFCN** & **Time Stamp** & **Frequency** & **eNodeB\_ID** \\ \hline
476 & 97 & 44280 & 151 & 275 & 11 January 2023 07:49 & 2137.5 & 701115 \\ \hline
447 & 41 & 19792 & 157 & 50 & 11 January 2023 06:03 & 2115 & 694173 \\ \hline
447 & 41 & 19792 & 157 & 50 & 11 January 2023 06:01 & 2115 & 694173 \\ \hline
666 & 0 & 19792 & 157 & 50 & 11 January 2023 01:43 & 2115 & 710557 \\ \hline
666 & 0 & 19792 & 157 & 50 & 11 January 2023 01:42 & 2115 & 710557 \\ \hline
465 & 95 & 48376 & 292 & 3250 & 11 January 2023 01:17 & 2670 & 702139 \\ \hline
566 & 17 & 44140 & 460 & 275 & 11 January 2023 12:03 & 2137.5 & 692923 \\ \hline
566 & 17 & 44140 & 151 & 275 & 11 January 2023 12:03 & 2137.5 & 692923 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._Regarding the mechanism for identifying suspicious base stations, it relies on MCC-MNC values. While our detection system efficiently filters out signals from these rogue cells due to their nonconformity with the whitelist and legitimate MCC-MNC configurations, determined attackers may seek to collect pertinent information beforehand to manipulate their transmission parameters. Given the inherently illicit nature of these attacks, practical considerations suggest that, when the detector reports unknown or suspicious base stations, the network operators should respond through various means.  
In the future, the detection scheme proposed in this research could be integrated with other methods, such as GPS-based signal source location as proposed by other researchers, to assist in identifying rogue base stations or in utilizing PCI information for automatic neighbor relations [21], among other approaches. We encourage readers to propose alternative methods or platforms for implementing and enhancing the proposed solution. Considering diverse real-world applications, including IoT use cases, beyond just smart healthcare is essential.  
Furthermore, it is advisable to conduct further signal analysis using diverse detectors or alternative detection methods. The comprehensive scrutiny of detailed records in comparison with the most up-to-date network information is recommended. If possible, the continuous observation of patterns over time, ideally across multiple records, provides a more comprehensive understanding of the situation.  
Validation, H.-C.H.; Writing--original draft, M.-H.L.; Writing--review & editing, I.-H.L.; Supervision, J.-S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Council (NSTC) in Taiwan under contract numbers 111-2221-E-006-079- and 112-2634-F-006-001-MBK.  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the dataset containing base station signal records that do not comply with regulatory approvals.  
The authors declare no conflict of interest.