Predictive Model for Pre-Execution Vulnerability Detection

The system performs preliminary actions by training a predictive model in advance using fuzz testing and security analysis to generate labeled training data. This model is ready before vulnerabilities are exploited, enabling immediate prediction and blocking of malicious inputs without waiting for traditional patch deployment. The predictive model is trained offline with extensive security testing data, so when a vulnerability is discovered, the system can immediately use the model to predict and prevent exploitation attempts.

The predictive model serves as an intermediary between the vulnerability and the exploitation attempt. Instead of directly patching the vulnerability in the program code, the system inserts a predictive model that analyzes inputs and predicts whether they would exploit the vulnerability. This intermediary layer blocks malicious inputs before they reach the vulnerable code, providing protection without modifying the original program.

The system creates copies of the vulnerable program in controlled testing environments to generate training data. Instead of directly analyzing the original program, multiple copies are executed with various inputs during fuzz testing to collect labeled examples of vulnerable and non-vulnerable inputs. This copying approach enables comprehensive security analysis without risking the original system and generates sufficient training data for accurate model prediction.

The system performs self-service by automatically generating training data through automated fuzz testing and security analysis. The predictive model trains itself using the labeled data generated from executing the program with various inputs and analyzing the results. This self-service approach reduces manual intervention and complexity in the training process while maintaining high prediction accuracy through extensive automated testing.