Amatul Raheem Ruqayyah

About

Computer Science student interested in cybersecurity, particularly security monitoring, threat detection, and incident investigation. I have hands-on exposure to tools such as Wazuh, Snort, Wireshark, and Nmap through projects and internship experience. I’m looking to build my skills in a SOC or cybersecurity team, contribute to identifying and investigating potential threats, and grow into a well-rounded security professional

Experience

• Cybersecurity Intern
  Codec Technologies
  Dec 2025 – —

  Conducted automated security audits and system threat analyses, identifying and documenting technical vulnerabilities to strengthen infrastructure defense. Simulated incident response protocols and collaborated with cross-functional teams to improve operational stability and system resilience.

Education

• 2028
  Integrated BS-MS in Computer Science

  Trendz Degree College

  Awarded by Osmania University

  Computer Science · Sep 2023 – Sep 2028

  Computer Science · Sep 2023 – Sep 2028

Skills

Tools / apps / platforms

Languages

Projects

• Security Monitoring & Attack Simulation Infrastructure
  VirtualBox, Snort, Wazuh

  Architected a virtualized multi-node environment using VirtualBox to analyze traffic flows and test system defense boundaries. Configured Snort and Wazuh engines to automate continuous log ingestion, real-time alert triggers, and system anomaly detection.

• Real-Time Human Detection & Surveillance Engine
  Python, Computer Vision

  Developed a high-accuracy Python computer vision pipeline for automated real-time object detection in dynamic environments. Fine-tuned model parameters to minimize false positives under high-noise and low-visibility conditions.

• Threat Ledger & Incident Analytics Engine
  Data Logging, Analysis

  Built a structured data logging and analysis framework to record, classify, and track operational security indicators. Streamlined system audit workflows by automating telemetry aggregation and threat intelligence indexing.

• Automated Deepfake Detection System (Urdu Media Focus)
  Python, Deep Learning

  Engineered a specialized deep learning engine to identify AI-manipulated synthetic media in regional language datasets. Implemented multi-model forensic analysis algorithms to evaluate video/audio integrity and counter digital misinformation.

🏆 Achievements & awards

• 1st Place Winner, Hackathon

  Awarded top placement for rapid application prototyping, system design, and execution under tight time constraints.