Adversarial ML in 5G Networks (Research)
Overview
A research study on the adversarial risks of using AI/ML for 5G network automation. The work covers supervised, unsupervised, and reinforcement-learning attack surfaces through three case studies, proposes mitigation approaches, and offers guidelines for evaluating ML model robustness in 5G contexts. Published in IEEE Internet Computing 2021. Outcome: Peer-reviewed publication that provides the 5G research community with a structured view of adversarial ML risks and mitigation strategies.
Architecture & Pipeline
flowchart LR
n0["5G ML ModelsSupervised · Unsupervised · RL"]
n1["Adversarial AttackCustom Python envs"]
n2["Robustness EvaluationThree case studies"]
n3["Mitigation GuidelinesBest practices"]
n4["Published FindingsIEEE Internet Computing 2021"]
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n1 --> n2
n2 --> n3
n3 --> n4
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class n1 step1;
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End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- Three case studies covering supervised, unsupervised, and RL attacks
- Custom Python environments for each adversarial scenario
- Mitigation guidelines and robustness evaluation framework
- Peer-reviewed publication in IEEE Internet Computing
- Tech Stack:** Python, TensorFlow