Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Faranak Abri
Second Advisor
Sayma Akther
Third Advisor
Narayan Balasubrmanian
Keywords
Topic Modeling, Large Languge Models, Social Engineering
Abstract
Detecting social engineering attempts is crucial for security, as these threats are becoming more frequent and increasingly exploit human vulnerabilities. This research focuses on topic modeling using conversational data from Kevin Mitnick’s ”The Art of Deception” with dialogues that illustrate various social engineering strategies. The dataset comprises manually extracted and synthetically augmented conversations to ensure natural dialogue flow. Two methodologies are presented for utterance-level and global topic extraction: prompt engineering leveraging OpenAI’s GPT-4o-mini, characterized by few-shot learning and chain-of-thought prompting, and Quantized Low Rank Adaptation (QLoRA) utilizing Mistral’s 7B instruct model for efficient fine-tuning. Through experimentation and evaluation, this study aims to determine the effectiveness of topic modeling approaches in enhancing the identification and classification of social engineering scenarios
Recommended Citation
Bhasin, Manav, "Real-time Adaptive Framework for Topic Modeling in Social Engineering Attacks" (2025). Master's Projects. 1477.
DOI: https://doi.org/10.31979/etd.bj8b-vfwz
https://scholarworks.sjsu.edu/etd_projects/1477
