Predicting Software Vulnerabilities with Advanced Computational Models

Abstract

Predicting software flaws is a key part of making current software systems safer and more reliable. The goal of this study is to find out how advanced computer models can be used to predict software weaknesses so that possible security risks can be found and reduced before they happen. Machine learning techniques, such as deep learning and ensemble methods, are used in the study to look at code trends, past data on vulnerabilities, and software measurements. These models are taught to find small signs of security holes that might not be obvious using normal static analysis methods. The suggested method includes a big sample with data from many different open-source and paid software projects. This makes sure that the research is fair and varied. To make the models more accurate at making predictions, feature extraction methods are used. These include natural language processing for code comments and dependency analysis for third-party libraries. The study also uses oversampling and cost-sensitive learning to deal with the problem of datasets that aren't fair, which comes up a lot when trying to predict vulnerabilities. The testing results show that the advanced computer models are good at predicting vulnerabilities with high accuracy and memory. The models not only find known flaws very accurately, but they also work well with code they haven't seen before, which shows they could be used in the real world. The study also shows how important it is to keep learning and changing the models to keep up with how software development methods change and new security threats appear. This study adds to the field of software security by creating a strong framework for predicting software flaws. This framework can be used throughout the software development process to improve security generally.