School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
PLA Army Academy of Artillery and Air Defense, Zhengzhou 450052, China.
Sensors (Basel). 2022 Feb 7;22(3):1265. doi: 10.3390/s22031265.
As one of the most effective methods of vulnerability mining, fuzzy testing has scalability and complex path detection ability. Fuzzy testing sample generation is the key step of fuzzy testing, and the quality of sample directly determines the vulnerability mining ability of fuzzy tester. At present, the known sample generation methods focus on code coverage or seed mutation under a critical execution path, so it is difficult to take both into account. Therefore, based on the idea of ensemble learning in artificial intelligence, we propose a fuzzy testing sample generation framework named CVDF DYNAMIC, which is based on genetic algorithm and BI-LSTM neural network. The main purpose of CVDF DYNAMIC is to generate fuzzy testing samples with both code coverage and path depth detection ability. CVDF DYNAMIC generates its own test case sets through BI-LSTM neural network and genetic algorithm. Then, we integrate the two sample sets through the idea of ensemble learning to obtain a sample set with both code coverage and vulnerability mining ability for a critical execution path of the program. In order to improve the efficiency of fuzzy testing, we use heuristic genetic algorithm to simplify the integrated sample set. We also innovatively put forward the evaluation index of path depth detection ability (pdda), which can effectively measure the vulnerability mining ability of the generated test case set under the critical execution path of the program. Finally, we compare CVDF DYNAMIC with some existing fuzzy testing tools and scientific research results and further propose the future improvement ideas of CVDF DYNAMIC.
作为漏洞挖掘最有效的方法之一，模糊测试具有可扩展性和复杂路径检测能力。模糊测试样本生成是模糊测试的关键步骤，样本的质量直接决定了模糊测试器的漏洞挖掘能力。目前，已知的样本生成方法侧重于关键执行路径下的代码覆盖或种子突变，因此很难兼顾两者。因此，基于人工智能中的集成学习思想，我们提出了一种名为 CVDF DYNAMIC 的模糊测试样本生成框架，它基于遗传算法和 BI-LSTM 神经网络。CVDF DYNAMIC 的主要目的是生成具有代码覆盖和路径深度检测能力的模糊测试样本。CVDF DYNAMIC 通过 BI-LSTM 神经网络和遗传算法生成自己的测试用例集。然后，我们通过集成学习的思想将这两个样本集集成起来，为程序的关键执行路径获得具有代码覆盖和漏洞挖掘能力的样本集。为了提高模糊测试的效率，我们使用启发式遗传算法来简化集成样本集。我们还创新性地提出了路径深度检测能力的评估指标（pdda），可以有效地衡量在程序的关键执行路径下生成的测试用例集的漏洞挖掘能力。最后，我们将 CVDF DYNAMIC 与一些现有的模糊测试工具和科研成果进行了比较，并进一步提出了 CVDF DYNAMIC 的未来改进思路。
