Abstract

With the rapid development of intelligent vehicles, more and more attention has been paid to vehicle safety. The electrical and electronic architecture of modern cars is also changing.

Car networks are also becoming more complex. We usually call them "buses".

Compared with traditional vulnerability mining, vehicular bus is difficult to rely on manual work because it generates millions of signals per second. And many functions do not have specific performance, black box is particularly difficult.

Therefore, it is very important to find out the security problem of the complex network inside the car. This topic describes some of the effective attack surfaces in Automotive. And our fuzzification framework is effective in practice.

Agenda:

1. Our presentation will reveal the attack surface of vehicular networks. Such as CAN network, FlexRay network, Lin network, Ethernet network and so on.
2. Next, we'll explain what threat scenarios these attack surfaces have. Such as cross-domain attack, malicious message, replay attack, DOS attack; CAN, FLEXRAY, and UDS protocol stack attacks.
3. Next, we will introduce the UDS protocol and the vehicle network protocol stack. Such as DOCAN, DoflexRay, DOIP and so on.
4. In particular, the internals of the flexray protocol are almost unknown. It has many difficulties.
5. And how to analyze these protocols in a black box environment.
6. Next, we will introduce how to use blur to explore complex vehicular black box networks. For example, CAN Fuzz, FlexRay Fuzz, UDS Fuzz.
7. We will demonstrate a real case.
8. We will open the source code of our fuzzy code.