Enhancing a machine learning-based social media threat intelligence automotive framework

This project will involve the design and implementation of a comprehensive data processing pipeline for detecting cybersecurity threats, specifically tampering attacks, in the automotive sector. The student will work on collecting and analyzing social media data using advanced data science techniques to extract meaningful insights about emerging security risks. This will involve processing unstructured text data, identifying relevant patterns, and utilizing large language models (LLMs) for text analysis and threat identification. The technical work will require the student to develop systems for data crawling, data cleaning, and text analysis, followed by the integration of AI models for detecting specific threat indicators. The project will provide experience in data engineering, machine learning, and natural language processing (NLP).