## 6 Security Analysis and Discussion
### Security Enhancement Analysis  
SMART DShot's ML-enhanced approach addresses fundamental limitations of traditional static security mechanisms through substantial risk reduction across all attack categories. Quantitative security improvements demonstrate the transformation of previously undetectable attack vectors into observable and mitigatable threats, with traditional CRC-based validation providing zero protection against timing manipulation attacks while SMART DShot achieves detection rates of 81.0-91.0% in sophisticated attack scenarios. As demonstrated in Figure 9, the CVSS severity score comparison shows a substantial reduction in risk from High (7.3 average) to Low (3.1 average) severity across all attack categories, representing a fundamental improvement in the security posture of the system.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Baseline CRC** & **SMART DShot** & **Improvement** \\ \hline Attack Detection Rate & 14.7\% & 88.3\% & +520\% \\ Timing Accuracy & \(\pm\)20 μs & \(\pm\)0.8 μs & +96\% \\ Response Time & N/A & \textless{}10 ms & New capability \\ Resource Overhead & 0\% & 2.16\% & Minimal impact \\ Power Increase & 0\% & 16.6\% & Acceptable trade-off \\ \hline \hline \end{tabular}  
* N/A’ indicates that the metric does not apply to the baseline, which does not perform active detection or recovery, making false positive rate and recovery time because these features are only applicable by ML feature at SMART DShot.  
\end{table}
Table 7: Comparative performance summary.  
Unlike static threshold-based approaches, SMART DShot's ML algorithms adapt to attack pattern evolution through the HATC meta-learning component, enabling dynamic response to novel attack characteristics and providing resilience against zero-day vulnerabilities. Real-time attack detection capability enables immediate response before system compromise, with detection latencies ranging from 0.8 ms for burst interference to 18.9 ms for firmware compromise, and recovery times spanning 6.8 ms to 15.9 ms, ensuring all response times remain well below the 50 ms threshold for maintaining flight control authority.  
To address detection latency concerns for safety-critical applications, SMART DShot implements several timing optimization strategies: (1) Priority-based attack classification: Our Neural Network Engine (Figure 6) prioritizes burst interference and timing manipulation attacks for sub-millisecond response (\(<\)0.8 ms), relegating complex firmware analysis to background processing, (2) Predictive buffering using Kalman filter state prediction: The KFTC algorithm (Equation (1)) provides 3-5 frame lookahead capability, enabling proactive compensation during detection delays, (3) Parallel processing pipeline optimization: Our FPGA implementation's 3.1\(\times\) timing margin (Table 4) provides headroom for additional acceleration, reducing critical path latency through hardware parallelization, (4) Operational mode adaptation: High-Security Mode achieves \(<\)10 ms average detection latency across all attack types by activating all four ML algorithms simultaneously, while Efficiency Mode prioritizes \(<\)5 ms response for time-critical applications, and (5) Statistical attack pattern pre-loading: The system maintains signature databases for known attack vectors, reducing detection time for previously characterized threats. These mitigation strategies ensure that safety-critical applications can achieve the required response times while maintaining the comprehensive security coverage demonstrated in our evaluation.