Overview:

This project aimed to automate the emergency doors lockdown process at Stevens using Microsoft Power Automate, improving security response times in critical situations.

Context:

Stevens Police Department needed a stable solution for initiating a full campus lockdown. The existing process required connecting to a VPN, logging into a server, and manually initiating lockdown mode for all campus doors controlled by a Transact System. This multi-step process was time-consuming and inefficient in emergency situations.

Challenge:

The manual steps involved in initiating a campus-wide lockdown delayed response times and required multiple actions that could complicate the process during an emergency.

Solution:

Using Microsoft Power Automate, I developed a cloud-based flow that mimicked the entire process of logging into the client and initiating the lockdown mode. To enhance security, we installed the flow directly on the main server and restricted access to a single individual’s Stevens account, protected by 2FA. We also integrated a physical button compatible with Power Automate for faster activation of the lockdown.

Implementation Details:
Input:

• Power Automate Flow: The flow automates logging into the Transact System client and initiating the lockdown.
• Physical Button: A compatible button that triggers the flow when pressed.

Flow Breakdown:

• Cloud Flow Automation: The flow logs into the Transact System, navigates to the door monitoring client, and triggers the lockdown sequence.
• Security Enhancements: Access was restricted to the Stevens Police account, which was protected by two-factor authentication (2FA).

Output:

The automation drastically reduced the time to initiate a lockdown and simplified the process for Stevens Police.

Impact:

The Police department appreciated the solution, and after successful testing, the physical button was implemented as a quick and secure way to trigger a campus-wide lockdown.

Overview:

The objective of this project was to automate the verification of network subnet boundaries during the configuration of an active network mapping tool. This script compares original network configurations against exported data from the tool, streamlining the process and reducing manual effort.

Context:

As part of adding a new network mapping tool to our infrastructure, the network team defined the network boundaries, which the security team needed to verify. The task involved comparing two large Excel sheets: one with the original configurations and another with the tool’s exported data. Manual comparison was inefficient, so I developed a Python script to automate this process.

Challenge:

Manually cross-checking hundreds of rows of network subnets in Excel was time-consuming and prone to error, making the process cumbersome.

Solution:

The solution involved creating a Python script that automatically compares the network addresses between the two sheets and identifies any mismatches. Below is a detailed breakdown of the approach:

Implementation Details:
Input:

• 2 Excel File:
  One for the original subnet list and another for the exported data from the tool.
• Columns:
- List 1: Original subnet configurations.
- List 2: Exported configurations from the network tool.

Script Breakdown:

• IP/Subnet Extraction: The script extracts the IP address and subnet from each entry using a regular expression:

• Network Calculation: Converts the IP and subnet into a network address:

• Comparison Logic: Compares the extracted network addresses from List 2 with those from List 1:

Output:

The script produces a status report that adds a new column to the Excel sheet, indicating whether each network address is correctly configured or not.

Sample Example:

Here's a small example of how the script checks for mismatches

Overview:

The objective of this project was to automate the cleaning of raw vulnerability data from Qualys, making it compatible with Grafana for data visualization. The automation reduced manual effort and made the process more efficient.

Context:

During a Vulnerability Assessment and Penetration Testing (VAPT) task, we were using Grafana to visualize vulnerability data. However, the raw output from Qualys was incompatible with Grafana’s validation requirements, requiring manual cleaning of the data to make it suitable for visualization.

Challenge:

Manually cleaning the raw data from Qualys for multiple scan result files was a tedious process, taking at least an hour each time.

Solution:

To streamline the process, I developed a Python script to automate the cleaning of Excel files. The script removed unnecessary columns, reformatted the data to meet Grafana’s validation requirements. After rigorous testing over three months, we implemented the automation for weekly use, saving approximately two hours per week as the script now completes the process in a matter of seconds.

Implementation Details:
Input:

• Raw output Excel files from Qualys containing vulnerability data.

Key Automations:

• Deleting unnecessary rows: The script automatically skips the first few rows in each file that contain irrelevant information not required for Grafana ingestion.

• Renaming columns: The script changes column headers (e.g., renaming ‘Asset Tags’ to ‘Asset Group’) to match Grafana's requirements.

• Adjusting risk scores: The script updates the risk score in certain cases, such as when an asset is classified as "Internet Facing," ensuring accurate data representation.

• Removing unnecessary tags: The script filters and removes tags not relevant to the analysis, leaving only essential tags for Grafana's dashboard.

• VLOOKUP implementation: A VLOOKUP function is included to merge additional data (such as server types) from a separate lookup file into the main file.

Output:

The script outputs a cleaned Excel file that is compatible with Grafana for direct use in dashboards.

Impact:

The automated solution saved up to two hours of manual work per week, allowing the security team to focus on higher-value tasks. The script now processes multiple scan result files in a matter of seconds.