Recognizing security patterns in code using machine learning – Python case study

The paper presents a vulnerability detection system based on deep learning that automatically learns features from a large set of real-world code. The dataset was collected from Github and contains Python code with various vulnerabilities, which has been adapted for CuBERT model. Data samples were created from the source code of vulnerable files by analyzing individual code tokens and their context, allowing for a detailed analysis. Pre-trained CuBERT model is fine-tuned on samples for each type of vulnerability to recognize the characteristics of vulnerable code and is then applied to detect vulnerabilities in source code.