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Connected cars - the security challenge:
Vehicles, Networks & Protocols
Ofer Rivlin
Product Security Lead @ Cyberark
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Mobile apps & Keyless entry
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Radars & Vision
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Computer Vision -
Slight alterations to an image, invisible to humans
can result in wildly different interpretations from a machine learning algorithm
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RADARs
 Accuracy (size & cost)
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RADARs
 Accuracy (size & cost)
 Interference (returning signals?)
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RADARs
 Accuracy (size & cost)
 Interference (returning signals?)
鐃 Mitigation: Uniqueness by different frequency/encoding
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RADARs
 Accuracy (size & cost)
 Interference (returning signals?)
鐃 Mitigation: Uniqueness by different frequency/encoding
 Privacy  Fingerprinting
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RADARs
 Accuracy (size & cost)
 Interference (returning signals?)
鐃 Mitigation: Unique frequency/encoding
 Privacy  Fingerprinting
鐃 Mitigation: Non-Unique properties
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RADARs
 Accuracy (size & cost)
 Interference (returning signals?)
鐃 Mitigation: Unique frequency/encoding
 Privacy  Fingerprinting
鐃 Mitigation: Non-Unique properties
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VANET/V2X
Current Security State
V2X Revocation Protocol
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Cooperative Intelligent Transportation Systems
(cITS)
detect insider attacks
based on attacker behavior
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V2X Revocation Protocol
 Revocation of misbehaving vehicles in a V2X network
 Vehicles that perform harmful cyber security activities
 messages with spoofed positions, incorrect speeds, etc.
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Suggested Latest Solutions
OSR (Order for Self-Revocation)
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Identified issues with latest protocols
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Secure By Design using formal methods
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Connected Cars  Security in Future
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https://zerodium.com/program.html
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Thank You!

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Connected cars - the security challenge: Vehicles, Networks & Protocols