KEYWORD |
Telecommunication Networks Group
Theses at Politecnico
Generalizable language models for network measurement and cybersecurity
keywords AI, CYBERSECURITY, FIREWALL, LLM, MACHINE LEARNING
Reference persons MARCO MELLIA, LUCA VASSIO
External reference persons Idilio Drago - Unito - idilio.drago@unito.it
Research Groups DATABASE AND DATA MINING GROUP - DBDM, SmartData@PoliTO, Telecommunication Networks Group
Description Firewall/IPS and EDR and Cloud security services analyze huge amounts of structured data to detect and classify threats, mainly based on human-written rules.
The thesis goal is to understand if lightweight and generalizable language models can extract insights from raw data. A key objective is to ensure generalization abilities beyond syntactic heuristics. A possible solution is to create multi-modal embeddings to conceptually constrain the embeddings towards the right task.
Thesis Goal
- Propose techniques based on language models for identify network traffic threats
- Ensure generalization beyond simple rule by crafting proper training and validation data
- Use techniques based on multi-modal embeddings (similar to OpenAI CLIP)
Required skills - Good programming skills (such as Python and Spark)
- Machine Learning knowledge (such as Torch, Tensorflow)
- Basics of NLP
- Basics of Networking and security
Notes A GPA of at least 27/30 is preferred.
Possible graduation prize of 2000 euros.
Deadline 14/01/2026
PROPONI LA TUA CANDIDATURA