Fix directory sturcture and added a new script

Author: ymeloch

README.md

@@ -218,6 +218,8 @@ This is important because when the radius.meraki.com cert is renewed, if devices
 
 **removetemplate.py:** This is script to create a copy of a template-based network that preserves as many of the network's settings as possible, while not relying on a configuration template. The initial focus of the script is converting MX appliance networks.
 
+**remoteAccessLogsAnalyzer.py:** This script fetches and analyzes Meraki Secure Connect remote access logs from the Meraki API and generates statistics for selected columns. The results are displayed in a color-coded table, and the data can be saved to a CSV file.
+
 **set_client_tracking.py:** A script to set the client tracking method of a group of networks to a desired value.
 
 **setlocation.py:** Sets the street address and optionally the map marker of all devices in a network or organization. To be more easily clickable, devices will be placed in a spiral around a seed location. There is an option to preserve marker location for MR access points, to avoid breaking wireless map layout.

secure_connect/README.md renamed to secure_connect/Private_Applications_CSV_Import/README.md

@@ -0,0 +1,57 @@
+# Meraki Secure Connect Remote Access Logs Analyzer
+
+This script fetches and analyzes Meraki Secure Connect remote access logs from the Meraki API and generates statistics for selected columns. The results are displayed in a color-coded table, and the data can be saved to a CSV file.
+
+## Requirements
+
+- Python 3.6+
+- `pandas` library
+- `termcolor` library
+- `requests` library
+
+Install required libraries using the following command:
+
+```bash
+pip3 install pandas termcolor requests
+```
+
+## Usage
+
+Run the script from the command line:
+
+```bash
+python3 remoteAccessLogsAnalyzer.py
+```
+
+You will be prompted to enter your Meraki organization ID, API key, and the desired date range (in epoch time).
+
+The script will fetch remote access logs from the Meraki API, parse them, and generate a DataFrame with the following columns:
+
+- Timestamp
+- Connect Timestamp
+- Connection Event
+- OS Version
+- AnyConnect Version
+- Internal IP
+- External IP
+
+The Connection Event and OS Version columns are color-coded for better readability. "connected" values are displayed in green, while "disconnected" values are displayed in red. Windows OS versions are displayed in yellow, and other OS versions are displayed in blue.
+
+The script will also generate statistics for the following columns:
+
+- OS Version
+- AnyConnect Version
+- Connection Event
+- Internal IP
+
+The generated data can be saved to a CSV file with the creation date in the file name.
+
+## Maintainers & Contributors
+
+[Yossi Meloch](mailto:[email protected])
+
+## Acknowledgements
+
+- [Cisco Meraki](https://www.meraki.com/) for providing a robust and easy-to-use API
+
+Please note that this script is provided "as is" without warranty of any kind, either expressed or implied, including limitation warranties of merchantability, fitness for a particular purpose, and noninfringement. Use at your own risk.

secure_connect/privateApplicationsImport.csv renamed to secure_connect/Private_Applications_CSV_Import/privateApplicationsImport.csv

@@ -0,0 +1,103 @@
+import requests
+import getpass
+import time
+import pandas as pd
+from termcolor import colored
+
+pd.set_option('display.max_columns', None)  # Display all columns
+pd.set_option('display.max_colwidth', None)  # Don't truncate column content
+pd.set_option('display.max_rows', None)  # Display all rows
+pd.set_option('display.width', None)  # Adjust display width to fit all columns
+
+def input_epoch_time(prompt):
+    """Helper function to prompt the user for an epoch time input."""
+    while True:
+        user_input = input(prompt)
+        try:
+            epoch_time = int(user_input)
+            return epoch_time
+        except ValueError:
+            print("Invalid input. Please enter a valid epoch time.")
+
+def save_dataframe_to_csv(df):
+    """Save the given DataFrame to a CSV file with the creation date in the file name."""
+    file_name = f"meraki_log_{time.strftime('%Y-%m-%d_%H-%M-%S')}.csv"
+    df.to_csv(file_name, index=False)
+    print(f"Data saved to {file_name}")
+
+def main():
+    """Main function to fetch and analyze Meraki remote access logs."""
+    # User input for Meraki organization ID
+    organization_id = input("Enter your Meraki organization ID: ")
+
+    # User input (masked) for Meraki API key
+    api_key = getpass.getpass("Enter your Meraki API key: ")
+
+    # User input for start and end date in epoch time
+    starting_after = input_epoch_time("Enter the start date in epoch time: ")
+    ending_before = input_epoch_time("Enter the end date in epoch time: ")
+
+    # Verify that the input time should be less than 30 days
+    time_difference = ending_before - starting_after
+    if time_difference > 30 * 24 * 60 * 60:
+        print("Error: The time range should be less than 30 days.")
+        return
+
+    # Construct the API request
+    base_url = 'https://api.meraki.com/api/v1'
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json'
+    }
+    endpoint = f'/organizations/{organization_id}/secureConnect/remoteAccessLog'
+    params = {
+        't0': starting_after,
+        't1': ending_before
+    }
+
+    # Send the API request and process the response
+    response = requests.get(base_url + endpoint, headers=headers, params=params)
+    if response.status_code == 200:
+        nested_data = response.json()
+        events = nested_data['data']
+        df_events = pd.json_normalize(events)
+
+        # Convert timestamp columns to datetime format
+        if 'timestamp' in df_events.columns:
+            df_events['timestamp'] = pd.to_datetime(df_events['timestamp'], unit='s')
+        if 'connecttimestamp' in df_events.columns:
+            df_events['connecttimestamp'] = df_events['connecttimestamp'].apply(
+                lambda x: int(float(x)) if not pd.isna(x) else x
+            )
+            df_events['connecttimestamp'] = pd.to_datetime(df_events['connecttimestamp'], unit='s')
+
+        # Color "connected" values in green and "disconnected" values in red
+        if 'connectionevent' in df_events.columns:
+            df_events['connectionevent'] = df_events['connectionevent'].apply(
+                lambda x: colored(x, 'green') if x == 'connected' else colored(x, 'red')
+            )
+
+        # Color "win-" values in orange and "mac-" values in blue
+        if 'osversion' in df_events.columns:
+            df_events['osversion'] = df_events['osversion'].apply(
+                lambda x: colored(x, 'yellow') if x.startswith('win-') else colored(x, 'blue')
+            )
+        
+        # Display all columns in expanded form
+        with pd.option_context('display.max_columns', None):
+            print(df_events)
+
+        # Generate statistics for selected columns
+        columns = ['osversion', 'anyconnectversion', 'connectionevent', 'internalip']
+        for col in columns:
+            stats = df_events[col].value_counts()
+            print(f"\n{col.capitalize()} Statistics:\n{stats}\n")
+        
+        # Save DataFrame to CSV file with creation date in the file name
+        save_dataframe_to_csv(df_events)
+    else:
+        print(f"Error: {response.status_code}")
+
+
+if __name__ == '__main__':
+    main()