Development of a software vulnerability prediction web service based on artificial neural networks

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Çatal, Çağatay
Ekenoğlu, Ecem
Alemdaroğlu, Meltem

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Detecting vulnerable components of a web application is an important activity to allocate verification resources effectively. Most of the studies proposed several vulnerability prediction models based on private and public datasets so far. In this study, we aimed to design and implement a software vulnerability prediction web service which will be hosted on Azure cloud computing platform. We investigated several machine learning techniques which exist in Azure Machine Learning Studio environment and observed that the best overall performance on three datasets is achieved when Multi-Layer Perceptron method is applied. Software metrics values are received from a web form and sent to the vulnerability prediction web service. Later, prediction result is computed and shown on the web form to notify the testing expert. Training models were built on datasets which include vulnerability data from Drupal, Moodle, and PHPMyAdmin projects. Experimental results showed that Artificial Neural Networks is a good alternative to build a vulnerability prediction model and building a web service for vulnerability prediction purpose is a good approach for complex systems.



Vulnerability Prediction, Artificial Neural Networks, Machine Learning, Web Service, Prediction Model, Vulnerabilities