security, privacy and protection in different vanet applications mario gerla
TRANSCRIPT
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Security, privacy and protection in different VANET applications
Mario Gerla
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Vehicular application and security requirements - Outline
• VANETs Introduction• VANET Applications
– safe navigation (sensor =>actuator) – minimal (other speaker will focus on this)
– content distribution/uploading– collaborative markets, etc– urban sensing (Mobeyes)
• Threat model and different privacy/security/protection requirements
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What is a VANET?
Penetration will be progressive (over 2 decades or so)
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Vehicular communications: why?
Most of these problems can be solved by providing appropriate information to the driver or to the vehicle
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Urban “opportunistic” vehicle ad hoc networking
From Wireless toWired networkVia Multihop
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Opportunistic piggy rides in the urban meshPedestrian transmits a large file in blocks to passing cars,
bussesThe carriers deliver the blocks to the hot spot
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Car to Car communications for Safe Driving
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 65 mphAcceleration: - 5m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc.
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.
Alert Status: None
Alert Status: Passing Vehicle on left
Alert Status: Inattentive Driver on Right
Alert Status: None
Alert Status: Slowing vehicle aheadAlert Status: Passing vehicle on left
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DSRC*/IEEE 802.11p : Enabler of Novel Applications
• Car-Car communications at 5.9Ghz
• Derived from 802.11a • three types of channels:
Vehicle-Vehicle service, a Vehicle-Gateway service and a control broadcast channel .
• Ad hoc mode; and infrastructure mode
• 802.11p: IEEE Task Group that intends to standardize DSRC for Car-Car communications
* DSRC: Dedicated Short Range Communications
F o r w a r d r a d a r
C o m p u t i n g p l a t f o r m
E v e n t d a t a r e c o r d e r ( E D R )
P o s i t i o n i n g s y s t e m
R e a r r a d a r
C o m m u n i c a t i o n f a c i l i t y
D i s p l a y
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Hot Spot
Hot Spot
Vehicular Grid as Opportunistic Ad Hoc Net
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Hot Spot
Hot Spot
PowerBlackout
ST O P
PowerBlackout
ST O P
Vehicular Grid as Emergency Net
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PowerBlackout
ST O P
PowerBlackout
ST O P
Vehicular Grid as Emergency Net
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CodeTorrent: Content Distribution using
Network Coding in VANETUichin Lee, JoonSang Park,
Joseph Yeh, Giovanni Pau, Mario GerlaComputer Science Dept, UCLA
ACM MobiShare 2006
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Content Distribution in VANET
• Multimedia-based proximity marketing:– Virtual tours of hotel rooms– Movie trailers in nearby theaters
• Vehicular ad hoc networks (VANET):– Error-prone channel– Dense, but intermittent connectivity – High, but restricted mobility patterns– No guaranteed cooperativeness (only, users of the same
interests will cooperate)• How do we efficiently distribute content in VANET?
– Traditional approach: BitTorrent-like file swarming
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BitTorrnet-like File Swarming• A file is divided into equal sized blocks• Cooperative (parallel) downloading among peers
From Wikipedia
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Swarming Limitation: Missing Coupon!
C1 Sends Block 1
C3C2C1
C6C5C4
B1
B1
C3 Sends Block 2
B2
B2
C2 Sends Block 2
B1 B2
B2
B2
C5 Sends Block 2
B2
B2
B2
B1 is STILL missing!!
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Network Coding• Let a file has k blocks: [B1 B2 … Bk] • Encoded block Ei is generated by
– Ei = ai,1*B1 + ai,2*B2 + … + ai,k*Bk
– ai,x : randomly chosen over the finite field• Any “k” linearly independent coded blocks can recover [B1
B2 … Bk] by matrix inversion• Network coding maximizes throughput and minimizes
delaya1,1=1
a1,2=0
Coded Block10E1
Coded Block11E2
Matrix Inversion
B110
B201
B1
B2
a2,1=1
a2,2=1
Network coding over the finite field GF(2)={0,1}
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Network Coding Helps Coupon Collection
C1 Sends Block 1
C3C2C1
C6C5C4
B1
B1
C3 Sends Block 2
B2
B2
C2 Sends a Coded Block: B1+B2
B1 B2B2
B1+B2
B1+B2B1+B2
B1
C5 Sends a Coded Block: B1+B2
B1+B2 B1+B2
B1+B2
B2 B1
C4 and C6 successfully recovered both blocks
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Previous Work: Cooperative Downloading with CarTorrent
Internet
Downloading Blocks from AP
Exchange Blocks via multi-hop pulling
G
RY
Y2
Gossiping Availability of Blocks
YY
Y
RRR
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CodeTorrent: Basic Idea
Internet
Downloading Coded Blocks from AP
Outside Range of AP
Buffer
BufferBuffer
Re-Encoding: Random Linear Comb.of Encoded Blocks in the Buffer
Exchange Re-Encoded Blocks
Meeting Other Vehicles with Coded Blocks
• Single-hop pulling (instead of CarTorrent multihop)
“coded” block
B1
File
: k b
lock
s
B2B3
Bk
+
*a1
*a2*a3
*ak
Random Linear Combination
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Design Rationale• Single-hop better than multihop
– Multi-hop data pulling does not perform well in VANET (routing O/H is high)
– Users in multi-hop may not forward packets not useful to them (lack of incentive)!
• Network coding– Mitigate a rare piece problem– Maximize the benefits of overhearing
• Exploits mobility – Carry-and-forward coded blocks
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FleaNet : A Virtual Market Place on Vehicular Networks
Uichin Lee, Joon-Sang Park Eyal Amir, Mario Gerla
Network Research Lab, Computer Science Dept., UCLA
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Advent of VANETs• Emerging VANET applications
– Safety driving (e.g., TrafficView)– Content distribution (e.g., CarTorrent/AdTorrent)– Vehicular sensors (e.g., MobEyes)
• What about commerce “on wheels”?
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Flea Market on VANETs
• Examples– A mobile user wants to sell “iPod Mini, 4G”– A road side store wants to advertise a special offer
• How to form a “virtual” market place using wireless communications among mobile users as well as pedestrians (including roadside stores)?
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Outline
• FleaNet architecture• FleaNet protocol design• Feasibility analysis• Simulation• Conclusions
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FleaNet Architecture-- System Components
• Vehicle-to-vehicle communications• Vehicle-to-infrastructure (ad-station) communications
Inter-vehic lecommunications
Private Adstation
Vehic le-to-adstationcommunications
* Roadside stores (e.g., a gas station)
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FleaNet Architecture -- Query Formats and Management
• Users express their interests using formatted queries– eBay-like category is provided
• E.g., Consumer Electronics/Mp3 Player/Apple iPod
• Query management– Query storage using a light weight DB (e.g., Berkeley DB)– Spatial/temporal queries– Process an incoming query to find matched queries (i.e.,
exact or approximate match)• E.g. Query(buy an iPod) Query(sell an iPod)
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FleaNet Protocol Design• FleaNet building blocks
– Query dissemination– Distributed query processing – Transaction notification
• Seller and buyer are notified• This requires routing in the VANET
• VANET challenges– Large scale, dense, and highly mobile
• Goal: designing “efficient, scalable, and non-interfering protocols” for VANETs
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Query Dissemination• Query dissemination exploiting vehicle mobility• Query “originator” periodically advertises its query to
1-hop neighbors– Vehicles “carry” received queries w/o further relaying
Q1
Q2
Q1
Q2
Yellow Car w/ Q1
Red Car w/ Q2
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Distributed Query Processing• Received query is processed to find a match of
interests– Eg. Q1 – buy iPod / QM – sell iPod / Q2 – buy Car
QM
QM
Q2
Q2
(1) Find a matching query for Q2
No match found
QM
LocalMatchQMQ1
(2) Send a match notification msg to the originator of query QM
Red car w/ Q2 & carries Q1
Cyan car w/ QM
Q1
(1) Find a matching query for QM
Found query Q1
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Transaction Notification• After seeing a match, use Last Encounter Routing
(LER) to notify seller/buyer– Forward a packet to the node with more “recent”
encounter
QM
LocalMatchQMQ1
Q1
Q1
Q1
Q1 T-1s
T-5s
T-10s
T
Encounter timestamp
Current Time: T
Originator of Q1
Cyan car
Red car
Blue car
Green carYellow carTRXRESP
TRXREQ
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FleaNet Latency
• Restricted mobility patterns are harmful to opportunistic data dissemination
• However, latency can be greatly improved by the popularity of queries
• Popularity distribution of 16,862 posting (make+model) in the vehicle ad section of Craigslist (Mar. 2006)
Freq
uenc
y (l
og)
Items (log)
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FleaNet Scalability• Assume that only the query originator can
“periodically” advertise a query to its neighbors• We are interested in link load• Load depends only on average number of neighbors
and advertisement period (not on network size)• Example:
– Parameter setting : R=250m, 1500B packet size, BW=11Mbps
– N=1,000 nodes in 2,400m x 2,400m (i.e., 90 nodes within one’s communication range)
– Advertisement period: 2 seconds– Worst case link utilization: < 4%
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Simulations• Ns-2 network simulator• 802.11b - 2Mbps, 250M radio
range• Two-ray ground reflection
model• “Track” mobility model
– Vehicles move in the 2400mx2400m Westwood area in the vicinity of the UCLA campus
• Metric– Average latency: time to find a
matched query of interest
Westwood area, 2400mx2400m
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Simulation Results
• Impact of density and speed
0
50
100
150
200
250
300
350
400
450
5 10 15 20 25
Average Speed (m/s)
Late
ncy
(S
eco
nd
s) N=100N=200N=300
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Simulation Results• Impact of query popularity
– Popularity: the fraction of users with the same interest– For a single buyer, increase the number of sellers (e.g., N=200/0.1 =
20 sellers)
0
10
20
30
40
50
60
70
0.05 0.1 0.15 0.2 0.25
Popularity
Late
ncy
(S
eco
nds)
N=100/V=5
N=100/V=25
N=300/V=5
N=300/V=25
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Simulation Results• Impact of ad-station location
– Given N=100, fix each node in its initial location, and set it as a “stationary” ad-station (as a buyer)
– measure the average latency to the remaining 99 mobile nodes (run 99 times, by taking turns as a seller: 1 buyer 1 seller)
0
50
100
150
200
250
300
350
400
450
500
1 11 21 31 41 51 61 71 81 91
Rank
Late
ncy
(S
eco
nds)
N=100/V=25m/s
avg. stationaryavg. mobile
Latency rank
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Epidemic Diffusion - Idea: Mobility-Assist Data Harvesting
Meta-Data Req
1. Agent (Police) harvestsMeta-Data from its neighbors
2. Nodes return all the meta-datathey have collected so far
Meta-Data Rep
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Threat Model and Security Requirements for VANET
applications
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The Threat Model
An attacker can be:• Insider / Outsider• Malicious / Rational• Active / Passive
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Attack 1 : Bogus traffic information
Attacker: insider, rational,active
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Attack 2 : Disruption of network operations
Attacker: insider, malicious,active
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Attack 3: Cheating with identity, speed, position
Attacker: insider, rational, active
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Attack 4: Jamming
Attacker: insider or outsider, malicious,active
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Attack 5: Tracking
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Security system requirements
Sender authenticationVerification of data consistencyAvailabilityNon-repudiationPrivacyReal-time constraints
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Security Architecture