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@@ -744,6 +744,22 @@ Key contributions of this work include:
     \item A systematic evaluation of the framework’s core algorithms, demonstrating robustness to real-world perturbations and scalability to millions of daily connection pairs.
 \end{itemize}
 
+The research is guided by several key questions, which are addressed below:
+
+\begin{enumerate}
+    \item \textbf{How can beaconing behavior be effectively detected within large-scale network data to provide early warning of potential threats?}
+    
+    Beaconing behavior can be effectively detected in large-scale network data by analyzing communication patterns for periodicity and regularity. By applying advanced signal processing techniques, such as FFT and ACF, the framework can identify subtle beaconing signals amidst noisy traffic. The multi-step validation process ensures high accuracy in distinguishing between benign and malicious periodic behavior, providing early warning of potential threats.
+
+    \item \textbf{What is the impact of periodicity in network communications on distinguishing between benign and malicious activities?}
+    
+    Periodicity in network communications plays an important role in distinguishing benign from malicious activities. While legitimate applications may exhibit periodic traffic patterns, malicious activities often involve irregular or concealed periodicity to evade detection. Analyzing the consistency and regularity of communication intervals aids in differentiating between benign and malicious behaviors.
+    
+    \item \textbf{Is the beaconing behavior detectable in generated synthetic data, and how does its detectability compare to that in real-world data?}
+    
+    Beaconing behavior is detectable in generated synthetic data; however, the detectability may differ from that in real-world data. Synthetic datasets often lack the complexity and variability found in real-world traffic, which can affect the performance of detection models trained exclusively on synthetic data. By evaluating the framework on both synthetic and real-world datasets, researchers can assess its robustness and generalizability across different environments.
+\end{enumerate}
+
 \section{Future Work}
 
 While the BAYWATCH framework has shown promising results, several avenues for future research could further enhance its capabilities and applicability: