Intelligent Fuzzing Technology Based on Combination Model of Multiple Reinforcement Learning Algorithms.

With the development of Internet of things technology, intelligent terminals of the Internet of things have gained popularity. At present, there are many security vulnerabilities in the firmware of the Internet of things terminal, and it is very inconvenient to use manual methods to detect the vulnerabilities of the Internet of things terminal equipment. The intelligent fuzzing technology based on genetic algorithms is mainly used, and the firmware to be tested is automatically tested using random variation data. Aiming at the low efficiency of the existing fuzzing technology based on genetic algorithms, this paper proposes an intelligent fuzzing model based on multiple reinforcement learning algorithms. In this model, reinforcement learning algorithms are used to optimize the mutation operator selection strategy of fuzzing and the code coverage of fuzzing is improved by intelligently selecting different mutation operators for different test cases. This paper compares the performance of DDQN, DDPG, TRPO, and PPO algorithms in the model through comparative experiments on LAVA datasets and traditional fuzzing methods. The results show that in the fuzzing environment, there are significant differences in the performance of different algorithms for different target programs and the fuzzing method based on reinforcement learning is obviously superior to the traditional fuzzing method, proving the proposed model' s availability and effectiveness. [ABSTRACT FROM AUTHOR]