Visible to the public Is Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms?

TitleIs Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms?
Publication TypeConference Paper
Year of Publication2018
AuthorsClemente, C. J., Jaafar, F., Malik, Y.
Conference Name2018 IEEE International Conference on Software Quality, Reliability and Security (QRS)
Date Publishedjul
ISBN Number978-1-5386-7757-5
KeywordsBayes methods, Bug Propensity Correlational Analysis, Computer bugs, Decision Tree, Decision trees, Deep Learning, deep learning technique, Feedforward Artificial Network, feedforward neural nets, learning (artificial intelligence), machine learning, Metrics, multilayer deep feedforward network, naive Bayes, Predictive models, program debugging, pubcrawl, Random Forest, security, security breaches, security metrics, security of data, security-related bugs, Software, software insecurity, software metrics, software quality, software quality metrics, software security bugs, Support vector machines
Abstract

Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naive bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.

URLhttps://ieeexplore.ieee.org/document/8424961
DOI10.1109/QRS.2018.00023
Citation Keyclemente_is_2018