Automotive Intrusion Detection · 2019-04-12 · Automotive Intrusion Detection/Prevention Attack...
Transcript of Automotive Intrusion Detection · 2019-04-12 · Automotive Intrusion Detection/Prevention Attack...
V1.0 | 2019-04-03
In Cooperation with the Institute for Information Processing Technologies (ITIV) – Karlsruhe Institute of Technology (KIT)Vector Cybersecurity Symposium 2019
Automotive Intrusion DetectionBenefits of a Static E/E Architecture combined with Machine Learning
© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
~100M lines of code in one vehicle [2]
Boeing 787 Dreamliner: ~14M lines of code [2]
Increased potential for safety-relevant attacks
History with summary of exploited interfaces
Automotive Megatrends
Attack Surface and Attack History
Motivation
Connectivity
~470M connected vehicles by 2025(E.U., U.S. and China) [1]
Autonomous Driving
~80M vehicles with high or full automation by 2030(E.U., U.S. and China) [1]
2010-
2014
2015
2016-
2018
Physical access to in-vehicle network, diagnostic port, multimedia interfaces, cellular network
„Jeep Hack“ via cellular network: Recall of 1.4M vehicles
Diagnostic port, multimedia interfaces, cellular network
[1] pwc, and strategy&. 2017. “The 2017 Strategy& Digital Auto Report: Fast and furious: Why making money in the "roboconomy" is getting harder.” https://www.strategyand.pwc.com/media/file/2017-Strategyand-Digital-Auto-Report.pdf.”
[2] “McCandless, David, Pearl Doughty-White, and Miriam Quick. 2015. “Codebases: Millions of lines of code.” https://informationisbeautiful.net/visualizations/million-lines-of-code/.”
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
Five Steps to Compromise an ECU
Attack Example
ADASDomain Controller
InfotainmentDomain Controller
Telematic Control UnitPowertrain
Domain Cont.
ChassisDomain Controller Body
DomainController
Intrusion Detection/Prevention System (IDPS)
Diagnostic port
1.Remote access
2.Access to
in-vehicle network
3.Bridge domain
boundaries
4.Access to
target ECU
5.Manipulate ECU orvehicle behavior
Defense barriers
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
The Big Picture
Automotive Intrusion Detection/Prevention
Attack
2. Report
Consolidation of security events, event storage and reporting
(e.g. hardware security module, secure communication, signed uploads)
3. Analyze
Threat monitoring and threat triage for single vehicles and the whole fleet
(e.g. impact analysis, root cause analysis)
4. Develop
Threat response (e.g. identification, implementation
and test of countermeasures)
5. Deploy
Secure download of software updates(e.g. secure communication, signed updates)
Security Operations Center (SOC)
1. Prevent and Detect
Intrusion prevention and detection sensors(e.g. firewalls, gateway, diagnostics,
watchdog, operating system)
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
Static electric/electronic (E/E) architecture
Definition of in-vehicle communication and to some extend also ECU internals in a semi-formalized way
[DBC, FIBEX, LDF]
AUTOSAR XML (ARXML)
Host-based (ECU internals)
Control flow
CPU runtime
Memory consumption
ECU-internal communication
Network-based (in-vehicle communication)
Ethernet
Controller Area Network (CAN)/CAN FD
[Local Interconnect Network (LIN)]
Automotive Intrusion Detection Principles
1. Prevent and Detect
Detection principles
Signature-based (detection of known attacks)
Anomaly-based (detection of deviations from normal behavior)
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
How to implement sensors without standardized information source?
Option 1: Extension of the ARXML format
ARXML format is already complex and difficult to maintain
Some properties cannot be specified in advance at all
Option 2: Usage of machine learning
Avoid additional specification and standardization efforts
Efficient combination of static checks and machine learning necessary> Machine learning not used as a replacement for
static checks but as a complement
➔ Deep dive: Plausibility sensor
Intrusion detection sensors (Müter et al. [3])
Intrusion Detection for Communication
1. Prevent and Detect
Nr. SensorStandardized
Information Source
S-1 Formality
S-2 Location
S-3 Range
S-4 Frequency
S-5 Correlation
S-6 Protocol
S-7 Plausibility
S-8 Consistency
✓
✓
✓
✓
✓
✓
[3] M. Müter, A. Groll, and F. C. Freiling, “A structured approach to anomaly detection for in-vehicle networks,” in Sixth International Conference on Information Assurance and Security (IAS), 2010. Piscataway, NJ: IEEE, 2010, pp. 92–98.
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
Input vector Ԧ𝑥 contains the last samples of a single communication signal (sliding window)
OCSVM: No online learning
LODA: False alarm rate in first tests >= 0,3% (~ one false alarm every 5-6 minutes in the test setup)
Machine Learning Mechanisms to Check Signal Plausibility
Deep Dive: Plausibility Sensor
Autoencoder a.k.a. Replicator Neural Network [6]
𝑝2(𝑥)
𝑝1(𝑥)
𝑝3(𝑥)
One Class Support Vector Machine (OCSVM) [4]
Lightweight On-line Detector of Anomalies (LODA) [5]
Autoencoder a.k.a.Replicator Neural Network [6]
Topology: 4-2-4
[4] B. Schölkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, “Support vector method for novelty detection,” in Advances in Neural Information Processing Systems 12. Cambridge, MA, USA: MIT Press, 2000, pp. 582–588.
[5] T. Pevný, “Loda: Lightweight on-line detector of anomalies,” Machine Learning, vol. 102, no. 2, pp. 275–304, 2016.
[6] S. Hawkins, H. He, G. Williams, and R. Baxter, “Outlier detection using replicator neural networks,” in Data Warehousing and Knowledge Discovery, ser. Lecture Notes in Computer Science, Y. Kambayashi,M. Arikawa, and W. Winiwarter, Eds. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2002.
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
Workflow (example)Working Principle
Training
TensorFlowTM used as framework for training and evaluation
Inference
Autoencoder
Deep Dive: Plausibility Sensor
Initial training and evaluation
Data pre-processing and split into different data sets(Training: ~76%, Validation: ~12%, Test ~12%)
[Results not promising]
Definition of empirical study(Variation of autoencoder topology)
Training
[Results promising]
Diagnostic data (~68h)
Synthesis of anomalies in signal curves and definition of evaluation metrics
Evaluation according to defined metrics
𝑂𝐹: Outlier Factor; 𝑂𝐹𝑆: Threshold
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
Synthesis of anomalies for evaluation
Definition of 13 anomaly types based on potential hardware failures (ISO 26262-5:2011 [7])
Random anomaly instances
Evaluation results
6-18-54-18-6 autoencoder
True positive rate: 85,8%
False positive rate: 00,0%
4-3-4 autoencoder
True positive rate: 78,5%
False positive rate: 00,0%
Anomalies and Evaluation Results
Deep Dive: Plausibility Sensor
[7]: International Organization for Standardization, Hrsg. Road vehicles – Functional safety – Part 5: Product development at the hardware level. 15. Nov. 2011.
All Anomaly TypesOriginal Signal
Signal with Anomalies
Time [s]
Norm
. vehic
le s
peed
Norm
. vehic
le s
peed
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Generation of automotive C code
No dynamic memory management, fixed point and floating point arithmetic, MISRA compliance
Optimized for execution time
Evaluation on a prototypical ECU (VC121)
Boundary conditions
120MHz, PowerPC, 32 Bit
4-3-4 autoencoder
32 Bit fixed point arithmetic
Plausibility check for one signal
~4 µs for one inference
20 Byte RAM
1112 Byte ROM
Prototypical Implementation
Deep Dive: Plausibility Sensor
Check plausibility of ~250 signals (10 ms cycle) with 10% additional CPU load
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Summary and Outlook
Automotive Intrusion Detection
2. Report
Consolidation of security events, event storage and reporting → need for standardization
3. Analyze
4. Develop
5. Deploy
Security Operations Center (SOC)
1. Prevent and Detect
Quick wins with static analysis
Advanced analysis by machine learning within ECUs
First step: Inference only (no online learning)
Collaboration model to be clarified> Who provides the necessary training data?
> Who trains the algorithm?
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© 2019. Vector Informatik GmbH. All rights reserved. Any distribution or copying is subject to prior written approval by Vector. V1.0 | 2019-04-03
Author:Weber, MarcVector Germany
Your questions are welcome!