A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities

DOI 10.1109/ase.2019.00164 dblp

Abstract

This paper presents a machine learning classifier designed to identify SQL injection vulnerabilities in PHP code. Both classical and deep learning based machine learning algorithms were used to train and evaluate classifier models using input validation and sanitization features extracted from source code files. On ten-fold cross validations a model trained using Convolutional Neural Network(CNN) achieved the highest precision (95.4%), while a model based on Multilayer Perceptron(MLP) achieved the highest recall (63.7%) and the highest f-measure (0.746).

BibTeX

@inproceedings{Zhang:ASE19,
  author    = {Kevin Zhang},
  title     = {A Machine Learning Based Approach to Identify {SQL} Injection Vulnerabilities},
  booktitle = {ASE},
  pages     = {1286--1288},
  publisher = {{IEEE}},
  year      = {2019},
}

A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities

Abstract

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