A Secure Ad-hoc Routing Approach using Localized Self-healing Communities Jiejun Kong Mario Gerla...
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A Secure Ad-hoc Routing Approach using Localized Self-healing Communities
Jiejun KongJiejun Kong, *Xiaoyan Hong, Yunjung Yi, Joon-Sang Park, *Jun Liu,
Mario GerlaMario Gerla WAM Laboratory
Computer Science Department *Computer Science DepartmentUniversity of California, Los Angeles University of Alabama, Tuscaloosa{jkong,yjyi,jspark,gerla}@cs.ucla.edu {jliu,hxy}@cs.ua.edu
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Problem Statement RREQ flooding attack by non-cooperative members
(selfish or intruded member nodes)
Direct RREQ floods– Non-cooperative members continuously generate RREQ– RREQ rate limited & packet suppression needed
Indirect RREQ floods – RREP & DATA packet loss
• Caused by rushing attack etc. [Hu et al.,WiSe’03]
– Indirectly trigger more RREQ floods• Don’t blame the RREQ initiator
Excessive floods deplete network resource
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Indirect Attack Example
RREQ forwarding– Rushing attackers disobey delay (MAC/routing/queuing) requirements
& w/ higher prob., are placed on RREP / DATA path– Can trigger more RREQ floods initiated by other good nodes
RREP & DATA packet loss is common in MANET– Hard to differentiate attackers from non-attackers;
network dynamics? non-cooperative behaviors?
source dest
RREQ
RREP
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Outline Related work
Community-based secure routing approach– Strictly localized– “Self-healing community” substitutes “single node”
Our analytic model– Asymptotic network security model– Stochastic model for mobile networks
Empirical simulation verification
Summary
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Related Secure Routing Approaches Cryptographic protections [TESLA in Ariadne, PKI in ARAN]
– Cannot stop non-cooperative network members;They have required credentials / keys
Network-based protections– Straight-forward RREQ rate limit [DSR, AODV]
• Long RREQ interval causes non-trivial routing performance degradation
– Multi-path secure routing [Awerbuch,WiSe’02] [Haas,WiSe’03]
• Not localized, incurs global overhead, expensive• Node-disjoint multi-path preferred, but challenging
– Rushing Attack Prevention (RAP) [Hu,WiSe’03]
• RREQ forwarding delayed and randomized to counter rushing• Causes large route acquisition delay; less likely to find optimal
path
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Our design Goal: minimize # of allowed RREQ floods
– Ideally, 1 initial on-demand RREQ flood for each e2e connection
– Maintain comparable routing performance
Solution:– Build multi-node communities to counter non-
cooperative packet loss– Design applies to wide range of ad hoc routing
protocols & various ad hoc networks
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Community: 2-hop scenario
Area defined by intersection of 3 consecutive transmissions Node redundancy is common in MANET
– Not unusually high, need 1 “good” node inside the community area Community leadership is determined by contribution
– Leader steps down (being taken over)if not doing its job (doesn’t forward within a timeout Tforw)
Community
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Community: multi-hop scenario
The concept of “self-healing community” is applicable to multi-hop routing
Communities
source dest
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Community Based Security (CBS)
End-to-end communication between ad hoc terminals Community-to-community forwarding (not node-to-node) Challenge: adversary knows CBS prior to its attack
– It would prevent the network from forming communities– Network mobility etc. will disrupt CBS
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On demand initial config Communities formed during RREP
– Simple heuristics: promiscuously overheard 3 consecutive (ACKs of) RREP packets set community membership flag for the connection
Goal revisited: reduce the need of RREQ floods– In spite of non-cooperative behavior
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Community around Vformed upon hearing RREPRREQ
RREP EV
V EU
On demand initial config around V
(Potentially non-cooperative) V’s community must be formed at RREP– Else V drops RREP and succeeds
– V1 and V2 need to know V’s “upstream”
V1
V2
upstream
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ACK-based configCommunities (if C forwards a correct RREP)
source destC
C’
C”
BD E
Communities(C’ and C” not in transmission range & C’ wins)
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Proactive re-config Each community loses shape due to network
dynamics (mobility etc.)
End-to-end proactive probing to maintain the shape– PROBE unicast + take-over– PROBE_REP unicast + take-over– Just like RREP
Again: reduce the need of RREQ floods– In spite of random mobility & non-cooperative behavior
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Re-config: 2-hop scenario
(PROBE, upstream, …)(PROBE_REP, hop_count, …)
Old community becomes staledue to random node mobility etc.
S D
oldF
newF
Newly re-configured community
Node D's roaming trace
X no ACK
PROBE
PROBE_REP
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Re-config: multi-hop scenario
Optimization– Probing message can be piggybacked in data packets– Probing interval Tprobe adapted on network dynamics
Simple heuristics: Slow Increase Fast Decrease
source dest
PROBE PROBE_REP
X no ACK
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Control flow & Data flow Control flows’ job
– Config communities: RREP– Reconfig communities: PROBE, PROBE_REP
(& data packets piggybacked with probe info)
– Unicast + take-over
DATA– DATA packets– Unicast + make-up (not take-over)
[community setup unchanged]
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Outline Other countermeasures
Community-based routing approach– Strictly localized w/ clearly-defined per-hop operation– “Self-healing community” substitutes “single node”
Our analytic model– Asymptotic network security model– Stochastic model for mobile networks
Empirical simulation verification
Summary
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Notion: Security as a “landslide” game Played by the guard and the adversary
– Proposal can be found as early as Shannon’s 1949 paper– Not a 50%-50% chance game, which is too good for the
adversary
The notion has been used in modern crypto since 1970s– Based on NP-complexity – The guard wins the game with 1 - negligible probability– The adversary wins the game with negligible probability– The asymptotic notion of “negligible” applies to one-way
function (encryption, one-way hash), pseudorandom generator, zero-knowledge proof, ……
AND this time ……
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Our Asymptotic Network Security Model Concept: the probability of security breach decreases
exponentially toward 0 when network metric increases linearly / polynomially
Consistent with computational cryptography’s asymptotic
notion of “negligible / sub-polynomial”
is negligible by definition
x is key length in computational cryptox is network metric (e.g., # of nodes) in network security
DefinitionDefinition: A function : N R is negligible, if for every positive integer c and all sufficiently large x’s (i.e., there
exists Nc>0, for all x>Nc),
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The Asymptotic Cryptography Model
Security can be achieved by a polynomial-bounded guard against a polynomial-bounded adversary
1 2 # of key bits (key length) 128
Prob
abili
ty o
f sec
urity
bre
ach The “negligiblenegligible” line
(sub-polynomialsub-polynomial line)
Insecure Secure(Ambiguous area)
• See Lenstra’s analysis for proper key length(given adversary’s brute-force computational power)
• There are approximately 2268 atoms in the entire universe
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Our Asymptotic Network Security Model
Conforming to the classic notion of security used in modern cryptography ! We’ve used the same security notion
Network metric (e.g., # of nodes -- network scale)
Prob
abili
ty o
f net
wor
k se
curit
y br
each
The “negligiblenegligible” line(sub-polynomialsub-polynomial line)
The “exponentialexponential” line
(memory-lessmemory-less line)
Insecure Secure(Ambiguous area)
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Mobile network model Divides the network into large number n of very
small tiles (i.e., possible “positions”)– A node’s presence probability p at each tile is small
Follows a spatial bionomial distribution B(n,p)
– When n is large and p is small, B(n,p) is approximately a spatial Poisson distribution with rate 1
– If there are N mobile nodes roaming i.i.d. N = N·1
– The probability of exactly k nodes in an area A’
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1 in Random Way Point model
[Bettstetter et al.]
a=1000
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Community area Aheal
(left) maximal community– 2-hop RREP nodes are (1 + )·R away– Area approaching
(right) minimal community– 2-hop RREP nodes are (2 - )·R away– Area approaching 0
Real world scenarios randomly distribute between these two extremes
A CB
A CB
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Modeling adversarial presence : percentage of non-cooperative network
members (e.g., probability of node selfishness & intrusion) 3 random variables
–x : number of nodes in the forwarding community area
–y : number of cooperative nodes
–z : number of non-cooperative nodes
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Effectiveness of CBS routing Per-hop failure prob. of community-to-community
routing is negligible with respect to network scale N
Per-hop success prob. of node-to-node ad hoc routing schemes is negligible (under rushing attack)
Tremendous gain EG := 1 / negligible approaching +1
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Community Based Security
In summary, in mobile networks haunted by non-cooperative behavior, community-based security has tremendous ( ) gain ( )
PcommunityPregular
N N
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QualNet simulation verification Perfermance metrics
– Data delivery fraction, end-to-end latency, control overhead
– # of RREQ x-axis parameters
– Non-cooperative ratio – Mobility (Random Way Point Model, speed min=max)
Protocol comparison– AODV: standard AODV– RAP-AODV: Rushing Attack Prevention (WiSe’03) – CBS-AODV: Community Based Security
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Performance Gap
CBS-AODV’s performance only drops slightly with more non-cooperative behavior
Tremendous EG justifies the big gap between CBS-AODV and others
%
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Mobility’s impact
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Less RREQ
In CBS-AODV, # of RREQ triggered is less sensitive to non-cooperative ratio
Enforcing RREQ rate limit is more practical in CBS-AODV
%
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Summary Conventional node-to-node routing is vulnerable to
routing disruptions– Excessive but protocol-compliant RREQ floods– Rushing attack + RREP / DATA packet loss
The new community-to-community secure routing is our answer– Analytic study approves the community design– Empirical simulation study justifies the analytic results– General design
Open challenges– More optimal estimation of forwarding window Tforw & probing interval
Tprobe – Secure and efficient key management between two communities
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This slide is intentionally left blank Backup slides follow
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1 Inspired by Bettstetter et al.’s work
– For any mobility model (random walk, random way point), Bettstetter et al. have shown that1 is computable following
– For example, in random way point model
in a square network area of size a£a defined by -a/2·x· a/2 and -a/2·y· a/2
– 1 is “location dependent”, yet computable in NS2 & QualNet given any area A’ (using finite element method)
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Delivery fraction & Control overhead
CBS-AODV’s performance only drops slightly with more non-cooperative behavior
Tremendous EG justifies the big gap (of delivery fraction & total control overhead) between CBS-AODV and others
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Latency
Route acquisition latency monotonically increases with
AODV’s avg. data packet latency drops due to short routes
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Mobility’s impact
CBS’s have better delivery fraction– CBS-AODV,cons_flood’s cost is too high
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RREQ limit control
In CBS-AODV, # of RREQ triggered is less sensitive to non-cooperative ratio
Enforcing RREQ rate limit is more practical in CBS-AODV
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Protocol Details
Packet format– (RREQ, upstream_node, ……)– (RREP, hop_count, ……)– In DSR or AODV , some of the extra fields can be spared
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Protocol Details
Unicast control packets & their ACKs
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Protocol Details
Unicast control flows config/re-config communities– RREP, PROBE, PROBE_REP packets & data packets piggybacked with probe info
– Unicast + take-over Data flows
– DATA packets– Unicast + make-up (not take-over)