Authors:
Claudio Curto
1
;
Daniela Giordano
1
;
Simone Palazzo
1
and
Daniel Indelicato
2
Affiliations:
1
Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), Università degli Studi di Catania, Catania, Italia
;
2
EtnaHitech Scpa, Catania, Italia
Keyword(s):
Vulnerability Detection, Machine Learning, Deep Learning, Transformer.
Abstract:
Research in software vulnerability detection has grown exponentially and a great number of vulnerability detection systems have been proposed. Recently, researchers have started considering machine learning and deep learning-based approaches. Various techniques, models and approaches with state of the art performance have been proposed for vulnerability detection, with some of these performing line-level localization of the vulnerabilities in the source code. However, the majority of these approaches suffers from several limitations, caused mainly by the use of synthetic data and by the inability to categorize the vulnerabilities detected. Our study propose a method to overcome these limitations, exploring the effects of different transformer-based approaches to extend the models capabilities while enhancing the vulnerability detection performance. Finally, we propose a transformer-based multitask model trained on real world data for highly reliable results in vulnerability detection
, CWE categorization and line-level detection.
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