强化学习引导模型生成的深度学习框架漏洞挖掘

Vulnerability Mining of Deep Learning Framework for Model Generation Guided by Reinforcement Learning

  • 摘要: 现有基于应用模型挖掘漏洞的方法随机生成模型的结构信息,容易造成大量低质测试用例的生成,严重影响漏洞挖掘的效率和效果.针对该问题提出了一种强化学习引导模型生成的深度学习框架漏洞挖掘方法.提取模型运行时的框架状态信息,包括Softmax距离、程序执行结果等,再将框架运行状态信息作为奖励变量指导模型结构与超参数的生成,进而提升测试用例的生成质量与效率.实验结果表明,在生成测试用例数量相同的条件下该方法能够发现更多深度学习框架的漏洞,实用价值高.

     

    Abstract: In the existing methods, the vulnerability mining is randomly generating the structural information of the model according to application model, generating easily a large number of low-quality test cases, and seriously affecting the efficiency and effect of vulnerability mining. To solve this problem, a vulnerability mining method of deep learning framework was proposed based on a guiding model generation method with reinforcement learning. Firstly, frame state information during model running was extracted, including Softmax distance and program execution results, etc. Then the extracted frame running state information was taken as a reward variable to guide the generation of model structure and hyper-parameters, so as to improve the quality and efficiency of test case generation. Experimental results show that this method can find more vulnerability of deep learning frameworks under the same number of generated test cases, possessing high practical value.

     

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