Endogenous Security Heterogeneous Entity Generation Method Based on Large Language Model
To address the security challenges posed by unknown vulnerabilities and backdoors in software systems,the paper proposed an endogenous security heterogeneous entity generation method based on large language models.This method,centered around endogenous security strategies,diversified the execution bodies of code that were vulnerable within the program,enabling the system to swiftly switch to a healthy heterogeneous entity upon attack,thereby ensuring stable operation.Furthermore,it leveraged large language models to generate a variety of heterogeneous entities and optimized existing fuzz testing techniques with a seed distance-based method,enhancing the quality of test case generation and code coverage rates,ensuring the functional equivalence of these heterogeneous entities.Experimental results demonstrate that this method can effectively repair code vulnerabilities and produce functionally equivalent heterogeneous entities.Additionally,compared to the existing AFL algorithm,the optimized fuzz testing method consumes less time to achieve the same code coverage rate.It is evident that the method put forward in the paper can significantly improve the security and robustness of software systems,offering a new strategy for the defense against unknown threats.
endogenous securitylarge language modelfuzz testing