Anas Abdulrahman Alshehri

Cybersecurity Specialization · Cohort 2026

Post-Quantum Secure Federated Intrusion Detection for Military UAV Networks

This project develops a post-quantum secure intrusion detection system for a group of five military UAVs connected to a central control centre. Each UAV trains its intrusion detection model locally using its own network traffic, allowing it to learn patterns of normal and malicious activity without exposing raw data. The UAV then securely sends only the trained model updates to the control centre, where federated learning combines them to improve the global intrusion detection model at control centre. These model updates are protected in transit using post-quantum cryptography, with ML-KEM for secure key establishment, ML-DSA for authentication, and AES-GCM for encrypted and integrity-protected transmission. The main beneficiaries are defence organisations, military UAV operators, and organisations managing security-sensitive autonomous or unmanned systems. The solution can be developed as a cybersecurity software product or managed security service for secure UAV fleet monitoring and collaborative intrusion detection. Its main advantage is the ability to improve cyberattack detection across multiple UAVs while keeping local data private and protecting communication against tampering and future quantum-related security risks.

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Artificial Intelligence Specialization · Cohort 2025

Machine Learning to Uncover the Oldest Light: U-Net Segmentation for Glitch Detection in Atacama Cosmology Telescope Data

The project develops and benchmarks advanced deep-learning modules that automatically clean and reconstruct the multi-terabyte timestreams recorded by the Simons Observatory and its precursor, the Atacama Cosmology Telescope. By suppressing instrumental noise and classifying foreground galaxies and stars, these models generate high-fidelity CMB maps while flagging potential astrophysical transients such as flaring stars and gamma-ray bursts. The work integrates convolutional, attention-based, and multi-modal techniques into the observatory’s production data-processing pipeline, laying the groundwork for scalable, real-time analysis of petabyte-scale cosmological data.

Mentor: Adam Hincks

Read the final report (PDF)

See it in the program report (PDF)