diff --git a/pages/EM_11_vulnerability_patching_web.py b/pages/EM_11_vulnerability_patching_web.py
index a665c15..797af96 100644
--- a/pages/EM_11_vulnerability_patching_web.py
+++ b/pages/EM_11_vulnerability_patching_web.py
@@ -21,7 +21,7 @@
 
 def generate_patching_graphs(df):
     try:
-        filtered_df = df[df['Software'].str.contains('Update for Windows 11', na=False)]
+        filtered_df = df[df['Software'].str.contains('Update for Windows 11|Update for Windows 10', na=False)]
 
         # We first need to convert the Microsoft DateTime stamp format: 20231215060522.240996-000 into a format we can perform calculations with in pandas
         # To do this we are stripping the 4 end characters and then using pandas datetime to make the conversion
@@ -77,7 +77,7 @@ def calculate_duration(group):
 def generate_updates_graph(df):
     try:
         def _categorize_software(software):
-            if 'Update for Windows 11' in software:
+            if 'Update for Windows 11' in software or 'Update for Windows 10' in software:
                 return 'Windows System Update'
             elif 'Security Intelligence Update for Microsoft Defender Antivirus' in software:
                 return 'Microsoft Defender Update'
@@ -91,7 +91,7 @@ def _categorize_software(software):
         df['SoftwareType'] = df['Software'].apply(_categorize_software)
 
         installed_updates = df[
-            ((df['Software'].str.contains('Update for Windows 11', na=False)) & (df['EventIdentifier'] == 'Installed')) |
+            ((df['Software'].str.contains('Update for Windows 11|Update for Windows 10', na=False)) & (df['EventIdentifier'] == 'Installed')) |
             ((df['Software'].str.contains('Security Intelligence Update for Microsoft Defender Antivirus', na=False)) & (df['EventIdentifier'] == 'Installed')) |
             ((df['Software'].str.contains('Update for Windows Security platform antimalware platform', na=False)) & (df['EventIdentifier'] == 'Installed')) |
             ((df['Software'].str.contains('Cumulative Update for .NET Framework', na=False)) & (df['EventIdentifier'] == 'Installed'))
diff --git a/pages/EM_12_reboot_analysis_web.py b/pages/EM_12_reboot_analysis_web.py
index d0ca068..c9ed3a0 100644
--- a/pages/EM_12_reboot_analysis_web.py
+++ b/pages/EM_12_reboot_analysis_web.py
@@ -83,7 +83,7 @@ def patch_to_reboot_analysis(df, df_v):
         earliest_versions = df.groupby('SoftwareVersion')['StartTime'].min().reset_index()
 
         # Clean the vulnerability dataframe and set the timestamp in a format pd can use
-        installed_updates = df_v[(df_v['Software'].str.contains('Cumulative Update for Windows 11', na=False)) & (df_v['EventIdentifier'] == 'Install Started')]
+        installed_updates = df_v[(df_v['Software'].str.contains('Cumulative Update for Windows 11|Cumulative Update for Windows 10', na=False)) & (df_v['EventIdentifier'] == 'Install Started')]
         installed_updates.loc[:, 'TimeGenerated'] = installed_updates['TimeGenerated'].str[:-4]
         installed_updates.loc[:, 'TimeGenerated'] = pd.to_datetime(installed_updates['TimeGenerated'], format='%Y%m%d%H%M%S.%f')
         installed_updates = installed_updates.drop(columns=['UpdateGUID', 'EventIdentifier'])