Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 17-22.doi: 10.6040/j.issn.1672-3961.0.2018.340

• Machine Learning & Data Mining • Previous Articles     Next Articles

The vulnerability mining method for KWP2000 protocol based on deep learning and fuzzing

Chengbin ZHANG1(),Hui ZHAO2,Zongyu CAO2   

  1. 1. College of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, Jiangsu, China
    2. National Trusted Embedded Software Engineering Technoloy Research Center, East China Normal University, Shanghai 200062, China
  • Received:2018-08-13 Online:2019-04-20 Published:2019-04-19
  • Supported by:
    江苏省前瞻性联合研究项目：基于物联网与深度学习的污水处理智能监控系统研究与开发(BY2016065-06)

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Abstract:

A kind of vehicle-onboard diagnosis Protocol standard, keyword protocol 2000 (KWP2000) KWP2000, was investigated in details. KWP2000 was widely used in the automobile industry and the loophole of possible communication Protocol. We analyzed the current situations of the fuzzing, and based on this, we proposed a generative adversarial networks (GAN) by deep learning neural network for automobile body network KWP2000 protocol hole mining method. The forward feedback network was closeted as the generation model, and the support vector machine was used as the discriminant model. We used the neural network model to train the test case data of the KWP2000 protocol data, the fuzzing of KWP2000 was carried out by using these test case data. Through experiments, we found that the target protocol KWP2000 had long loopholes, coding errors and other vulnerabilities. Experimental results showed that this fuzzing method was efficient and safe.

Key words: KWP2000, deep learning, generative adversarial nets, fuzzing, onboard diagnostic

CLC Number: 

  • TP18

Table 1

The unit format of the network layer protocol"

地址信息 协议控制信息 数据域
N_AI(1) N_PCI(2) N_Data(3)

Table 2

The PCI format corresponding four PDU of the ISO 15765 protocol network layer"

N_PDU
名称		Byte #1Byte#2 Byte#3
Bit#7-4 Bit#3-0
单帧(SF) N_PCItype=0 SF_DL N/A N/A
第一帧(FF) N_PCItype=1 FF_DL FF_DL N/A
连续帧(CF) N_PCItype=2 SN N/A N/A
流控制帧(FC) N_PCItype=3 FS BS STmin

Fig.1

The model of the generative adversarial network"

Fig.2

The model of the feed forward neural networks"

Fig.3

The schematic diagram of the support vector machine"

Fig.4

The generating adversarial network model for the KMP2000 protocol security test"

Fig.5

The structure diagram of the security test"

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