Hello I am Vaibhav Kumar , results-driven software developer with a strong background in cybersecurity and full-stack development. He has hands-on experience as a Software Intern at IBM, where he specialized in creating automated security dashboards and integrating machine learning models for real-time threat analysis. His work at IBM demonstrated his ability to improve system efficiency and strengthen security measures
Python, HTML/CSS, C++, JavaScript, Node.js
Express.js, Mongoose, React.js
MongoDB
Burp Suite, Nmap, Wireshark, Kali Linux, Metasploit, OWASP ZAP, OSINT Tools, SQLMap, Pwn Tools
GitHub
Vulnerability Assessment and Penetration Testing, Malware Analysis, Threat Hunting
Comptia Security+ (SYO-701), Ethical Hacking Essentials (EHE) from EC-Council, Network Defense Essentials (NDE) from EC-Council, Fundamentals of Information Security from Infosys
Passionate about ethical hacking and security research.
I developed an automated security dashboard designed to monitor vulnerabilities and threat trends. It uses machine learning to analyze network traffic and detect potential threats. I used Python scripts and APIs to collect real-time security logs and vulnerability reports.The data was processed using Pandas and NumPy to extract meaningful metrics like threat occurrences and severity levels.I built the dashboard using React.js and Chart.js, presenting visual insights through graphs and charts, allowing for easier interpretation of threat trends. To streamline reporting, I utilized: Python for data processing and analysis. Matplotlib and Seaborn to generate visual reports highlighting vulnerability trends.Jupyter Notebooks to create reusable reporting templates.The reports included metrics like severity distribution, impacted assets, and resolution timelines, which improved decision-making efficiency. I applied SMOTE (Synthetic Minority Over-sampling Technique) to balance the classes, which significantly reduced false positives by 20%.
GitHub LinkAdvanced IDS/IPS system using machine learning to detect zero-day attacks, Built using Python, Scapy.py, and Socket.io, the system leverages the Random Forest Algorithm to detect suspicious network activities. The integration of ML models not only improves detection accuracy but also reduces false positives by 20%. This system is crucial for safeguarding networks from emerging threats and maintaining robust security postures..
GitHub LinkReal-time packet tracker using TCP/IP and UDP protocols, developed using Python and the Socket-learn library to capture network packets in real time. It leverages TCP/IP and UDP protocols to track data transmission over networks. By utilizing efficient parsing algorithms, it reduces packet processing time by 30%. This project significantly enhances network monitoring capabilities by providing a clear visual representation of packet flow, helping network administrators detect anomalies and performance issues promptly.
GitHub LinkInteractive visualization tool for sorting algorithms like Merge Sort, Heap Sort, Insertion Sort, and more.
GitHub Link