CS MSc Thesis Zoom Presentation 2 February 2021
Plats: Online via: https://lu-se.zoom.us/j/68911960342
Kontakt: birger [dot] swahn [at] cs [dot] lth [dot] se
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One Computer Science MSc thesis to be presented on 2 February via Zoom
Tuesday, 2 February there will be a master thesis presentation in Computer Science at Lund University, Faculty of Engineering.
The presentation will take place via Zoom at: https://lu-se.zoom.us/j/68911960342
Note to potential opponents: Register as an opponent to the presentation of your choice by sending an email to the examiner for that presentation (firstname.lastname@example.org). Do not forget to specify the presentation you register for! Note that the number of opponents may be limited (often to two), so you might be forced to choose another presentation if you register too late. Registrations are individual, just as the oppositions are! More instructions are found on this page.
Presenters: Viktor Bard, Rasmus Lindqvist
Title: Vulnerability detection using ensemble learning
Examiner: Jacek Malec
Supervisors: Pierre Nugues (LTH), Emil Wåreus (Debricked)
Managing vulnerabilities in open-source software is becoming increasingly more important when the use of open-source is only increasing as a standard in software development. The vast amount of open-source versions makes manual inspection both impractical and costly. We propose and automated approach to vulnerability detection by using ensemble techniques. We benefit from models predicting on git commit messages and code changes, and use these in an ensemble together with meta data. Furthermore we explore the possibility to expand the ensemble by implementing a graph neural network to classify abstract syntax trees in code changes. Our logistic regression model shows an increase of F1 score by 4.95% from the best individual model in the ensemble. Our Neural Network model shows an increase of 4.7% in precision from previous models.
Link to presentation: https://lu-se.zoom.us/j/68911960342
Link to popular science summary: https://fileadmin.cs.lth.se/cs/Education/Examensarbete/Popsci/210202_13BardLindqvist.pdf