计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 318-324.doi: 10.11896/jsjkx.210300281
胡志濠, 潘祖烈
HU Zhi-hao, PAN Zu-lie
摘要： 目前,网络协议模糊测试的目标趋向于大型协议实体,而传统的测试用例过滤方法主要是基于测试对象的运行状态信息,测试对象越庞大,其执行单个测试用例的时间也越长。因此,针对传统的网络协议模糊测试用例过滤方法存在无效执行时间长、效率低下的问题,依据循环神经网络模型对序列数据较强的处理和预测能力,提出一种基于QRNN的网络协议模糊测试用例过滤方法。通过学习网络协议的结构特征,包括字段取值范围和字段间约束关系,该方法可以自动过滤无效测试用例,减少协议实体测试用例的执行次数。实验结果表明,与传统的网络协议模糊测试用例过滤方法相比,所提方法可以有效降低网络协议漏洞挖掘的时间成本,显著提高网络协议模糊测试的效率。
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