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rajsegar/README.md

Rajsegar Alagarathnam

Cybersecurity Graduate | Penetration Testing Portfolio | Security Research

Sunderland, United Kingdom · LinkedIn · TryHackMe · Medium · Email

About

MSc Cyber Security graduate (Distinction) with over four years of IT operations experience across systems administration, Active Directory, Microsoft 365, endpoint management, and network support. I am developing a practical portfolio in authorised web application testing, vulnerability assessment, Active Directory security, and AI security research.

I am currently preparing for the Certified Penetration Testing Professional (CPENT) certification and am open to junior penetration testing, cybersecurity analyst, and security research opportunities in the UK.

Core Skills

Area Capabilities
Offensive Security Vulnerability assessment, web application testing, OWASP Top 10, network scanning and enumeration, Active Directory security assessment, security reporting
Security Tooling Nmap, Burp Suite, Metasploit, Cobalt Strike, Nessus, OpenVAS, Wireshark, Kali Linux
Enterprise Technology Windows Server, Active Directory, Microsoft 365, Microsoft Intune, VMware, Barracuda appliances, Nagios, Uptime Robot, ServiceNow
Research Interests AI and LLM security, federated learning, adversary simulation, secure system design

Featured Projects

Project Summary
SME VAPT Framework MSc project that defines an end-to-end penetration-testing and vulnerability-assessment workflow for SMEs, from scoping and reconnaissance through validation, reporting, and controlled adversary simulation.
PoisonScope: Detecting Backdoored LLMs AI security research evaluating a transformer sentiment model against TextFooler and DeepWordBug attacks, with documented mitigations.
CPENT Project Portfolio Ongoing collection of CPENT study notes, lab methodology, evidence, and practical lessons learned.

Security testing featured in this portfolio is conducted only in authorised environments, labs, and CTF platforms.

Experience Highlights

Cybersecurity Researcher - CRAC Learning
AI Security Research | 2025

  • Compared federated-learning frameworks for simulation and enterprise use.
  • Implemented secure federation communications using TLS certificates, authentication keys, mutual TLS, and public-key allowlisting.

Intern System Administrator - Vital Hub Innovations Lab
Hybrid | 2022-2023

  • Administered Active Directory and Microsoft 365, including access controls, account lifecycle management, and group policies.
  • Supported endpoint hardening and device compliance with Microsoft Intune, VMware, Barracuda appliances, and operational monitoring.

Current Development

  • Preparing for the CPENT certification.
  • Deepening practical skills in web application testing, Active Directory attack paths, vulnerability validation, and remediation reporting.
  • Continuing research into AI/LLM security and secure federated learning.

Highlights

  • MSc Cyber Security (Distinction), University of Sunderland.
  • TryHackMe: ranked in the top 6% globally.
  • Published technical notes and security write-ups on Medium.

Contact

I welcome connections with cybersecurity professionals, recruiters, and researchers. You can reach me through LinkedIn or email.

Popular repositories Loading

  1. security-study-plan security-study-plan Public

    Forked from jassics/security-study-plan

    Complete Practical Study Plan to become a successful cybersecurity engineer based on roles like Pentest, AppSec, Cloud Security, DevSecOps and so on...

  2. Public-URL Public-URL Public

  3. Federated-Learning-Frameworks- Federated-Learning-Frameworks- Public

    FL Frameworks installation in AWS Environment

    Jupyter Notebook

  4. Spam_Mail_Detection Spam_Mail_Detection Public

    Spam_mail_dect

    Jupyter Notebook

  5. PoisonScope PoisonScope Public

    This tutorial provides a broad end-to-end overview of training, evaluating, and attacking a model using TextAttack.

    Jupyter Notebook

  6. -Capstone-Project-PoisonScope-Detecting-and-Analyzing-Backdoored-LLMs-on-Hugging-Face -Capstone-Project-PoisonScope-Detecting-and-Analyzing-Backdoored-LLMs-on-Hugging-Face Public

    Large language models (LLMs) are rapidly evolving, revolutionizing natural language processing (NLP) applications.

    Python 1