Date of Defense
6-11-2024 1:00 PM
Location
E1-1040
Document Type
Thesis Defense
Degree Name
Master of Science in Software Engineering
College
CIT
Department
Computer Science
First Advisor
Dr.Salah Bouktif
Keywords
Android Apps, Permissions, NLP, Machine Learning, Security, BERT, LSTM, Ensemble Models
Abstract
This study develops an NLP-based system to recommend essential permissions for Android apps by analyzing app descriptions. It leverages advanced models, including LSTM and ensemble techniques, to align permissions with app functionality while minimizing unnecessary requests.
Included in
PERMISSION RECOMMENDATION FOR ANDROID APPLICATIONS: LEVERAGING NATURAL LANGUAGE PROCESSING ON APP DESCRIPTIONS
E1-1040
This study develops an NLP-based system to recommend essential permissions for Android apps by analyzing app descriptions. It leverages advanced models, including LSTM and ensemble techniques, to align permissions with app functionality while minimizing unnecessary requests.