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 testing how well ML models hold up to attacks 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 a framework for evaluating attack resistance
- Peer-reviewed publication in IEEE Internet Computing
- Tech Stack: Python, TensorFlow