# Explainable AI Assisted IoMT Security in Future 6G Networks
## 7 Mitigation Strategies in IoMT-Driven 6G Usage Scenarios  
XAI insights play a vital role in enhancing the security of 6G-enabled medical IoT systems by transparently identifying critical features that influence both performance and potential vulnerabilities [11, 30]. In Table 9, we analyzed various 6G usage scenarios with the help of XAI methods, which allowed us to identify key features contributing to both attack and non-attack behaviors. XAI techniques revealed which specific features influenced security outcomes--highlighting whether a high or low value of a given feature was associated with malicious or normal activity. By mapping these feature-based insights onto the functional requirements of each 6G usage scenario, we were able to pinpoint potential threats relevant to that scenario. Based on this analysis, we proposed targeted mitigation strategies aligned with the critical system behaviors and security risks of each use case. Furthermore, we presented XAI-driven security enhancements that empower security administrators to extract actionable insights and implement precise, scenario-specific measures--ensuring robust protection tailored to the unique demands of each 6G medical IoT use case.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Usage Scenario** & **XAI Insights (SHAP,** & **Security Risk** & **Mitigation Strategy** & **Security Enhancements** \\
**LIME, DiCE)** & **Identified** & **Mitigation Strategy** & **from XAI Insights** \\ \hline \hline \multirow{8}{*}{**Immersive Communication**} & & & SHAP and LIME insights & support dynamic load \\  & & & & balancing in real-time \\  & & & & applications, reducing the risk of service disruption \\  & SHAP and LIME show & & & and improving system \\  & high CPU I/O wait and & & & balancing, anomaly \\  & memory usage. DiCE & & & detection systems, and \\  & shows abnormal byte & & spikes, and delayed \\  & transfer and jitter & & data access. & \\  & values. & & & data integrity [37, 38]. \\  & & & & \\  & & & & blockchain-based \\  & & & logging—enhancing secure \\  & & & & data transmission and \\  & & & threat response \\  & & & mechanisms. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mitigation strategies and security improvements for 6G usage scenarios based on XAI insights.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Usage Scenario** & **XAI Insights (SHAP,** & **Security Risk** & **Mitigation Strategy** & **Security Enhancements** \\  & **LIME, DiCE)** & **Identified** & **from XAI Insights** \\ \hline \hline \multirow{6}{*}{**High-Reliability** **Low-Latency** **Communication**} & SHAP show high user & & & SHAP identify signs of cryptojacking, prompting secure boot mechanisms to block unauthorized mining activities. LIME and DiCE \\  & CPU time and low idle & DDoS, cryptojacking, malware, and latency & & Behavioral-based detection, packet rate & detect abnormal packet sizes and flooding attempts, enabling proactive rate-limiting and behavioral firewalls to maintain system availability under high load conditions. \\ \hline \multirow{6}{*}{**High-Reliability** **Low-Latency** **Communication**} & LIME and SHAP detect & & & LIME’ detection of memory spikes facilitates memory leak identification and profiling. SHAP’ analysis of slab usage supports & \\  & usage (svmem\_active, svmem\_slab) and & Cryptojacking, network congestion, and & & Memory profiling tools, smart traffic filtering, and task scheduling on MEC [37; 39; 40]. & \\  & packet numbers. DiCE & & & \\  & detects traffic & & & MEC [37; 39; 40]. & \\  & anomalies. & & & \\  
Understanding the security implications of these attacks will help to safeguard emerging 6G technologies--ensuring resilient, safe, and intelligent healthcare systems in the future.