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Security, privacy and protection in different VANET applications
Mario Gerla
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
What is a VANET?
Penetration will be progressive (over 2 decades or so)
Vehicular communications: why?
Most of these problems can be solved by providing appropriate information to the driver or to the vehicle
Urban “opportunistic” vehicle ad hoc networking
From Wireless toWired networkVia Multihop
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
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
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
Hot Spot
Hot Spot
Vehicular Grid as Opportunistic Ad Hoc Net
Hot Spot
Hot Spot
PowerBlackout
ST O P
PowerBlackout
ST O P
Vehicular Grid as Emergency Net
PowerBlackout
ST O P
PowerBlackout
ST O P
Vehicular Grid as Emergency Net
CodeTorrent: Content Distribution using
Network Coding in VANETUichin Lee, JoonSang Park,
Joseph Yeh, Giovanni Pau, Mario GerlaComputer Science Dept, UCLA
ACM MobiShare 2006
14
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
15
BitTorrnet-like File Swarming• A file is divided into equal sized blocks• Cooperative (parallel) downloading among peers
From Wikipedia
16
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!!
17
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}
18
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
19
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
20
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
21
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
FleaNet : A Virtual Market Place on Vehicular Networks
Uichin Lee, Joon-Sang Park Eyal Amir, Mario Gerla
Network Research Lab, Computer Science Dept., UCLA
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”?
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)?
Outline
• FleaNet architecture• FleaNet protocol design• Feasibility analysis• Simulation• Conclusions
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)
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)
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
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
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
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
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)
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%
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
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
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
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
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
Threat Model and Security Requirements for VANET
applications
The Threat Model
An attacker can be:• Insider / Outsider• Malicious / Rational• Active / Passive
Attack 1 : Bogus traffic information
Attacker: insider, rational,active
Attack 2 : Disruption of network operations
Attacker: insider, malicious,active
Attack 3: Cheating with identity, speed, position
Attacker: insider, rational, active
Attack 4: Jamming
Attacker: insider or outsider, malicious,active
Attack 5: Tracking
Security system requirements
Sender authenticationVerification of data consistencyAvailabilityNon-repudiationPrivacyReal-time constraints
Security Architecture