山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 17-22.doi: 10.6040/j.issn.1672-3961.0.2018.340
Chengbin ZHANG1(),Hui ZHAO2,Zongyu CAO2
摘要:
为实现无需协议的任何结构知识进行网络安全漏洞检测,基于深度学习生成对抗式神经网络(generative adversarial nets, GAN),提出对车身网络关键字协议2000 (keyword protocol 2000, KWP2000)漏洞挖掘的方法。选用前向反馈网络作为生成模型,支持向量机作为判别模型。利用神经网络模型训练生成KWP2000协议数据的测试用例数据,再利用这些测试用例数据对KWP2000进行模糊测试。通过试验发现目标协议KWP2000的超长错误、编码错误等漏洞。试验研究表明,该模糊测试方法提高了效率和安全性。
中图分类号:
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