diff --git a/Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.pdf b/Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.pdf
index 8c9bfea0c66d63478aea32631a5ac7106936a3e6..837e09bdf1f1f50039c73620404875e37d421a91 100644
Binary files a/Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.pdf and b/Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.pdf differ
diff --git a/Thesis_Docs/main.tex b/Thesis_Docs/main.tex
index c313466ef0ae851a147875c33511533aef35059d..8db02bde8784002511d9cc174d60956274359c1a 100644
--- a/Thesis_Docs/main.tex
+++ b/Thesis_Docs/main.tex
@@ -49,7 +49,7 @@ This research aims to enhance APT detection capabilities by focusing on beaconin
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/apt_attack_lifecycle.png}
-    \caption{7 phases of APT attack lifecycle \cite{charan2021dmapt}}
+    \caption{7 Phases of APT Attack Lifecycle \cite{charan2021dmapt}}
     \label{fig:apt_attack_lifecycle}
 \end{figure}
 
@@ -70,7 +70,7 @@ Enterprise networks are the backbone of modern organizations, providing the nece
 \begin{figure}
     \centering
     \includegraphics[width=0.7\textwidth]{../Thesis_Docs/media/enterprise_network_diagram.png}
-    \caption{Enterprise network diagram}
+    \caption{Enterprise Network Diagram}
     \label{fig:enterprise_network_diagram}
 \end{figure}
 
@@ -144,7 +144,7 @@ The BAYWATCH framework consists of four main phases, each involving one or more
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/algorithm.jpg}
-    \caption{Algorithm steps}
+    \caption{BAYWATCH Algorithm Steps}
     \label{fig:steps}
 \end{figure}
 
@@ -465,7 +465,7 @@ Understanding the distribution and frequency of URL requests is for identifying
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/urls_request_count_log_scale.png}
-    \caption{Request counts of URLs (log scale). The X-axis represents URL hostnames index that is sorted in descending order, and the Y-axis shows the number of visits on a logarithmic scale.}
+    \caption{Request Counts of URLs (log scale). The X-axis represents URL hostnames index that is sorted in descending order, and the Y-axis shows the number of visits on a logarithmic scale.}
     \label{fig:requestcount}
 \end{figure}
 
@@ -478,7 +478,7 @@ Understanding the temporal patterns of URL visits is for analyzing user behavior
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/visit_in_24h.png}
-    \caption{Number of visits by hour (24 hours). The X-axis represents the hours of the day, and the Y-axis shows the number of visits.}
+    \caption{Number of Visits by Hour (24 hours). The X-axis represents the hours of the day, and the Y-axis shows the number of visits.}
     \label{fig:24hvisit}
 \end{figure}
 
@@ -493,7 +493,7 @@ Analyzing the time intervals between URL requests is important for identifying p
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/seconds.png}
-    \caption{Distribution of time intervals between URL requests (0–65 seconds). The X-axis represents time intervals (bins) in seconds, and the Y-axis shows the number of requests on a logarithmic scale.}
+    \caption{Distribution of Time Intervals Between URL Requests (0–65 seconds). The X-axis represents time intervals (bins) in seconds, and the Y-axis shows the number of requests on a logarithmic scale.}
     \label{fig:timeintervallog}
 \end{figure}
 
@@ -508,7 +508,7 @@ The identification of such periodic patterns is important in network traffic ana
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/minutes.png}
-    \caption{Distribution of time intervals between URL requests (in minutes). The X-axis represents time intervals (bins) in minutes (1--30+ mins), and the Y-axis shows the number of requests on a logarithmic scale.}
+    \caption{Distribution of Time Intervals Between URL Requests (in minutes). The X-axis represents time intervals (bins) in minutes (1--30+ mins), and the Y-axis shows the number of requests on a logarithmic scale.}
     \label{fig:timeintervallogmin}
 \end{figure}
 
@@ -526,7 +526,7 @@ Understanding the interaction patterns of hosts within the network is important
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/unic_urls.png}
-    \caption{Distribution of hosts based on unique URLs contacted. The X-axis represents the number of unique URLs contacted by each host, and the Y-axis shows the count of hosts in each category.}
+    \caption{Distribution of Hosts Based on Unique URLs Contacted. The X-axis represents the number of unique URLs contacted by each host, and the Y-axis shows the count of hosts in each category.}
     \label{fig:ip}
 \end{figure}
 
@@ -575,7 +575,7 @@ Figure \ref{fig:ip_url} illustrates the distribution of unique URLs accessed by
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/ip_url_chart.png}
-    \caption{Distribution of hosts based on unique URLs contacted. The X-axis represents the hosts, while the Y-axis shows the count of unique URLs each host connected to, displayed on a logarithmic scale in decreasing order.}
+    \caption{Distribution of Unique URLs Accessed by Different Hosts. The X-axis represents the hosts, while the Y-axis shows the count of unique URLs each host connected to, displayed on a logarithmic scale in decreasing order.}
     \label{fig:ip_url}
 \end{figure}