In the last decade, data security has become a primary concern for an increasing amount of companies around the world. Protecting the customer's privacy is now at the core of many businesses operating in any kind of market. Thus, the demand for new technologies to safeguard user data and prevent data breaches has increased accordingly. In this work, we investigate a machine learning-based approach to automatically extract sources and sinks from arbitrary Java libraries. Our method exploits several different features based on semantic, syntactic, intra-procedural dataflow and class-hierarchy traits embedded into the bytecode to distinguish sources and sinks. The performed experiments show that, under certain conditions and after some preprocessing, sources and sinks across different libraries share common characteristics that allow a machine learning model to distinguish them from the other library methods. The prototype model achieved remarkable results of 86% accuracy and 81% F-measure on our validation set of roughly 600 methods.

Sas, D., Bessi, M., Arcelli Fontana, F. (2018). Automatic detection of sources and sinks in arbitrary Java libraries. In Proceedings - 18th IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2018 (pp.103-112). Institute of Electrical and Electronics Engineers Inc. [10.1109/SCAM.2018.00019].

Automatic detection of sources and sinks in arbitrary Java libraries

Arcelli Fontana, F
2018

Abstract

In the last decade, data security has become a primary concern for an increasing amount of companies around the world. Protecting the customer's privacy is now at the core of many businesses operating in any kind of market. Thus, the demand for new technologies to safeguard user data and prevent data breaches has increased accordingly. In this work, we investigate a machine learning-based approach to automatically extract sources and sinks from arbitrary Java libraries. Our method exploits several different features based on semantic, syntactic, intra-procedural dataflow and class-hierarchy traits embedded into the bytecode to distinguish sources and sinks. The performed experiments show that, under certain conditions and after some preprocessing, sources and sinks across different libraries share common characteristics that allow a machine learning model to distinguish them from the other library methods. The prototype model achieved remarkable results of 86% accuracy and 81% F-measure on our validation set of roughly 600 methods.
No
paper
Java; Machine Learning; Sink; Source; Static Analysis;
Java; Machine Learning; Sink; Source; Static Analysis; Software; Safety, Risk, Reliability and Quality
English
18th IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2018
978-153868290-6
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8528960
Sas, D., Bessi, M., Arcelli Fontana, F. (2018). Automatic detection of sources and sinks in arbitrary Java libraries. In Proceedings - 18th IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2018 (pp.103-112). Institute of Electrical and Electronics Engineers Inc. [10.1109/SCAM.2018.00019].
Sas, D; Bessi, M; Arcelli Fontana, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/219124
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