A Machine Learning Approach for Context-Aware Input/Output Validation in Mobile Applications

The vulnerability of mobile applications to security risks due to inadequate input/output validation has been high-lighted. Conventional rule-based methods have found it difficult to adjust to changing attack surfaces and various environments. A unique method for context-aware input/output validation in mobile applications has been suggested, utilizing machine learning. The creation of a machine learning model that can instantly detect abnormal patterns by examining past data, app context, and user input sequences has been investigated. An adaptive validation system has been implemented based on the model's output, dynamically modifying the validation criteria. The efficacy and user experience of the system has been assessed through penetration testing and user research. It is anticipated that the findings will aid in the creation of mobile applications that are both safer and easier to use.