Oklahoma State UniversityAhmad SalehiyanIndustrial Engineer
← Back to Projects
Deep Learning

Transformer-Based Deep Learning for Anomaly and Intrusion Detection

Hybrid Transformer–GAN–Autoencoder evaluated on WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT (Future Internet, 2025); Transformer and deep reinforcement learning framework for false data injection detection (Computers, Materials & Continua, 2026).

Transformer-Based Deep Learning for Anomaly and Intrusion Detection

Timeline

2025–2026

Impact Focus

Published in Future Internet (2025) and Computers, Materials & Continua (2026).

Core Tools

deep learning, transformers, PyTorch

Problem

Anomaly, intrusion, and false data injection detection in engineering systems.

What I did

Developed hybrid Transformer–GAN–Autoencoder and evolutionary-optimized Transformer–deep reinforcement learning frameworks.

Result

Published in Future Internet (2025) and Computers, Materials & Continua (2026).

What changed

  • - Evaluated on WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT
  • - False data injection detection in smart water infrastructure

Deliverables

  • - Published in Future Internet (2025) and Computers, Materials & Continua (2026).

Stack

deep learning
transformers
PyTorch
Contact