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Jun Li
Twitter:@bravo_fighter
UnicornTeam
Qihoo360
Automobile Intrusion Detection
2
What this talk is
about?
Automotive intrusion detection
Automotive cyber-security architecture
3
From the highest viewpointJ
Outline
•  Quick recap of the status quo of
car security research
•  Little automobile working principle
•  CAN bus anomaly detection
Performance
Tuning by
modifying
firmware
Immobilizer
Cracking
(Hitag,
Keeloq)
DARPA&UW
OBD
interface
attack,etc.
Karl et al.
Remote
attack
via wireless
OBD
interface
Telsa
Qihoo360
BMW
ConnectedDrive
vuln
Mbrace Jeep Uconnect
Charlie&Chris
GM Onstar
Vuln,Sammy
More to
come ?
Sure!
Car hacking
development
Car explained
Sensor security
In automotive electronics, Electronic
Control Unit (ECU) is a generic term for
any secret system that controls one or
more of the electrical system or
subsystems in a transport vehicle
Types of ECU include Electronic/engine
Control Module (ECM), Powertrain Control
Module (PCM), Transmission Control
Module (TCM), Brake Control Module
(BCM or EBCM), Central Control Module
(CCM), Central Timing Module (CTM),
General Electronic Module (GEM),
Body Control Module (BCM), Suspension
Control Module (SCM), control unit, or
control module
ECU (Electronic Control Unit)
Electronic Control Module Example
9
Automotive Mechatronics
10
Non-hackable hackable
11
Throttle position sensor
Drive-by-wire system
12
Steering-by-wire system
Universal joint
Steer-by –wire
(with mechanical fallback clutch)
Automotive Control System Architecture
Vehicle CAN BUS System
Vehicle Communication System
OBDII
MOST
LIN
CAN
FlexRay
Bluetooth
Wifi
SubGHz
Infotainment
System
ESP TCU ACC
ESP(electronic stability program
TCU(transmission control unit)
ACC(adaptive cruise
control)
… CAN-C
网
Speedometer
CAN-B
Infotainment
System
Music Player
INS(Inertial navigation system
INS
EMU
EMU(engine management
system)
Seat Controller
Vehicle Communication System example
CAN BUS Signaling
CAN Frame Structure
0 dominant
1 recessive
1 1 1
1
01
1
1
1 0
0
0
1
1
1 0 0
0 0
0 0
CAN Bus Access Arbitration
Packets injection
Parameter spoofing
CAN BUS Attack
Jeep Uconnect Vulnerability
WiFifemotocellSprintInternet CAN
Remote Attack Example
Automotive intrusion detection researches
Automotive intrusion detection researches
Not considering
Temporal feature
Distributed
architecture
CAN总线安全防御模型
IDS
IDS(Intrusion Detection System)
① Real time requirements
② Hard to trace back to sender
③ High cost of false positive
④ …
Difficulties of CAN bus
defence
CAN Anomaly Detection McAfee&Intel
CAN bus defence
IDS
CAN security architecture
Bluetooth WiFi Cellular V2X
IDS
Experiment Car
•  Cellular Connection
•  Cloud Service
•  Bluetooth Key
•  Hybrid
•  Electronic Brake
•  Electric Power
Steering
•  Electronic Throttle
Experiment car’s CAN network
The CAN database
Why don’t we build a model
Take the relation ship of rpm and speed ,
gear for example,we can create a
model of the System‘s behavior
汽车工作原理
Anomaly detection system
Realtime data
stream
Cross
Prediction
Parameter
extraction
System model requirements
Gear
Build the system model
Data
Collection
Data
preprocess
Data
analysis
Feature
Selection
Model
Training
&Testing
Data
Acquisition Parameter presence on different BUS
Parameter	 Speed	
Engine	
RPM	
Acceleration	
Pedal	
Intake	
Pressure	
Brake	
Pedal	
Steering	
Wheel	
Gear	
BUS	
Instrument	 o	 o	 x	 x	 o	 o	 o	
Comfort	 o	 o	 x	 x	 o	 x	 x	
Power	 o	 o	 o	 o	 o	 x	 x	
ECM	 o	 o	 o	 o	 o	 x	 o	
ESC	 o	 o	 o	 x	 o	 o	 o
Data Acquisition Setup
Data Analysis Can database is kept highly confidential
Data Preprocess
Data Preprocess
Interpolation
Sampling
Normalization
Normalization
Must make sure the maximum and minimum value,don’t
calculate from the training data
数据插值
Observation
Interpolation
Sub-Sampling
Sub-SamplingTime_
ms
RPM Speed MAP MAF AccPeda
l Throttle
13897
3
0.287983
8
0.134259
2
0.059055
1
0.167567
5
0.697107
0
0.137795
2
13897
4
0.287312
5
0.134259
2
0.055118
1
0.167567
5
0.697107
0
0.137795
2
13897
5
0.287312
5
0.134259
2
0.051181
1
0.167567
5
0.697107
0
0.137795
2
13897
6
0.285970 0.134259
2
0.047244
0
0.167567
5
0.697107
0
0.137795
2
13897
7
0.285970 0.134259 0.051181
1
0.167567
5
0.697107
0
0.137795
2
Sub-Sampling
Model training
Model training
Results
Result
Model testing
Model testing
Acknowledgement
Professor Shuicheng Yan
Doctor Ming Lin
Doctor Zhanyi Wang
Doctor Lin Huang
Thank You!
Q&A
Reference
1.  Karl Koscher, Alexei Czeskis, Experimental Security Analysis of a Modern
Automobile, 2010
2.  Stephen Checkoway,Damon McCoy,Brian Kantor, Comprehensive Experimental
Analyses of Automotive Attack Surfaces,2011.
3.  Charlie Miller,Chris Valasek,Adventures in Automotive Networks and Control
Units,2013.
4.  Charlie Miller,Chris Valasek,Remote Exploitation of an Unaltered Passenger
Vehicle,2015
5.  Dieter Spaar,Sicherheitslücken bei BMWs ConnectedDrive/ Beemer, Open
Thyself! – Security vulnerabilities in BMW's ConnectedDrive,2015.
6.  Iamthecarvalry.org , Five Star Automotive Cyber Safety Framework,2015.
7.  Pierre Kleberger,Security Aspects of the In-Vehicle Network in the Connected
Car,IEEE Intelligent Vehicles Symposium,2011
8.  Marc Rogers,Kevin Mahaffey,How to Hack a Tesla Model S,DEF CON
23,2015
9.  Charlie Miller Chris Valasek,Advanced CAN Injection Techniques for Vehicle
Networks,BlackhatUSA,2016
10. Kyong-Tak Cho and Kang G. Shin, Fingerprinting Electronic Control Units for
Vehicle Intrusion Detection, 2016
11. Nobuyasu Kanekawa,X-by-Wire Systems,Hitachi Research Lab.2011
12. Paul Yih, Steer-by-Wire: Implication For Vehicle Handling and Safety,Stanford
PHD Dissertation,2005
13. Luigi Coppolion,Dependability aspects of automotive x-by-wire technologies,
2008.
14. Jonas Zaddach,Andrei Costin,Embedded Devices Security and Firmware Reverse
Engineering,Blackhat Workshop,2013.
15. Andrei costin,Jonas Zaddach,A large-Scale Analysis of the Security of
Embedded Firmwares,EURECOM,2014.
16. Samy Kamkar,Drive It Like You hacked It,DEF CON23,2015
17. David A Brown, Geoffrey Cooper, Automotive Security Best Practices, White
Paper by Intel & McAfee,2014.
18. OpenGarages, Car Hacker’s Handbook,openGarage.org,2014.
19. Henning Olsson, OptimumG,Vehicle Data Acquisition Using CAN,2010
20. Varun Chandola,Arindam Banerjee,Vipin Kumar,Anomaly Detection :A
Survey,2009
21. Park, Ming Kuang, Neural learning of driving environment prediction for vehicle
power management, Joint Conf. on Neural Networks, 2008.
22. Taylor, P., Adamu-Fika, F., Anand, S., Dunoyer, A., Griffiths, N., and Popham, T.
Road type classification through data mining,2012.
23. Michael Muter, Naim Asaj,Entropy-based anomaly detection for in-vehicle
networks", IEEE Intelligent Vehicles Symposium (IV), 2011.
24. Ulf E. Larson, Dennis K. Nilsson,An Approach to Specification-based Attack
Detection for In-Vehicle Networks, IEEE Intelligent Vehicles Symposium,2008.
25. Y. L. Murphey, Zhi Hang Chen, L. Kiliaris, Jungme ,I. Tang and T. P. Breckon,
Automatic road environment classication, IEEE Trans. on Intelligent
Transportation Systems, 2011.
26. Salima Omar, Asri Ngadi, Hamid H.Jebur, Machine Learning Techniques for
Anomaly Detection: An Overview.
27. Perter Harrington,Machine Learning In Action,2013.
28. Jurgen Schmidhuber, Deep learning in neural networks: An overview, 2015.
29. Kaiserslautern,Comparison of Unsupervised Anomaly Detection Techniques,
German Research Center for Artificial Intelligence, 2011
30. Sepp Hochreiter, Jurgen Schmidhuber, Long short-term memory,Neural
computation, 1997.
31. Michael Husken, Peter Stagge,Recurrent neural networks for time series
classifcation, Neurocomputing, 2003.
32. Felix A Gers, Jurgen Schmidhuber, Fred Cummins, Learning to forget:Continual
prediction with LSTM, Neural computation, 2000.
33. David E Rumelhart, Geo_rey E Hinton, and Ronald J Williams.,Learning
internal representations by error propagation,1985.
34. Christopher M Bishop,Pattern recognition and machine learning, springer, 2006.
35. Simon Haykin and Neural Network. A comprehensive foundation. Neural
Networks, 2004.
36. Eleazar Eskin,Andrew Arnold,Michael Prerau, A Geometric Framework for
Unsupervised Anomaly Detection-Detecting Intrusions in Unlabeled Data
tection-Detecting Intrusions in Unlabeled Data,2002.
37. Kingsly Leung, Christopher Leckie, Unsupervised Anomaly Detection in
Network Intrusion Detection Using Clusters, 2005

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