Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
PERFORMANCE ANALYSIS OF TRANSFORMER-BASED LANGUAGE MODELS FOR DETECTION OF PHISHING EMAILS |
| Συγγραφείς: |
Arannonu, Chidozie Princewill |
| Στοιχεία εκδότη: |
Purdue University Graduate School, 2026. |
| Έτος έκδοσης: |
2026 |
| Θεματικοί όροι: |
Data engineering and data science, Natural language processing, Deep learning, Adversarial machine learning, Machine learning not elsewhere classified, Cybersecurity and privacy not elsewhere classified |
| Περιγραφή: |
This thesis evaluates the performance and adversarial robustness of four transformer-based language models; BERT, RoBERTa, DistilBERT, and GPT-2 which were fine-tuned for binary phishing email classification. Two preprocessing configurations (Basic and Advanced) and five training data fractions are systematically compared using a temporally partitioned dataset that separates pre-2015 training data from 2015–2024 deployment data, simulating real-world generalization across evolving phishing tactics. DistilBERT with Advanced preprocessing achieves the best overall performance, reaching 97.26% accuracy and a weighted F1-score of 0.972. The study further conducts adversarial robustness evaluation using four attack strategies from the TextAttack framework namely; DeepWordBug, Pruthi, TextBugger, and TextFooler which found out that character-level attacks achieve success rates of up to 22.44%. Finally, applying Advanced preprocessing to adversarially perturbed inputs recovers post-attack accuracy to near-clean-data levels across all models, establishing preprocessing as a deployable defense mechanism rather than merely a data cleaning step. |
| Τύπος εγγράφου: |
Thesis |
| DOI: |
10.25394/pgs.32204841 |
| DOI: |
10.25394/pgs.32204841.v1 |
| Rights: |
CC BY |
| Αριθμός Καταχώρησης: |
edsair.doi.dedup.....0415be53f5ea962a4bcab77b0d2ed5a0 |
| Βάση Δεδομένων: |
OpenAIRE |