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A Secure Ad-hoc Routing Approach using Localized Self-healing Communities Jiejun Kong Mario Gerla Jiejun Kong, * Xiaoyan Hong, Yunjung Yi, Joon-Sang Park, * Jun Liu, Mario Gerla WAM Laboratory Computer Science Department * Computer Science Department University 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 T forw ) 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 V formed upon hearing RREP RREQ RREP E V V E U On demand initial config around V (Potentially non-cooperative) V ’ s community must be formed at RREP –Else V drops RREP and succeeds –V 1 and V 2 need to know V ’ s “ upstream ” V1V1 V2V2 upstream
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ACK-based config Communities (if C forwards a correct RREP) source dest C C’ C” B D 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 stale due 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 T probe adapted on network dynamics Simple heuristics: Slow Increase Fast Decrease source dest PROBEPROBE_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 crypto x is network metric (e.g., # of nodes) in network security Definition Definition: A function : N R is negligible, if for every positive integer c and all sufficiently large x’ s (i.e., there exists N c >0, for all x>N c ),
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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 Probability of security breach negligible sub-polynomial The “negligible” line (sub-polynomial line) Insecure Secure (Ambiguous area) See Lenstra’s analysis for proper key length (given adversary’s brute-force computational power) There are approximately 2 268 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) Probability of network security breach negligible sub-polynomial The “negligible” line (sub-polynomial line) exponential memory-less The “exponential” line (memory-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 A heal (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
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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 ( ) P community P regular 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 T forw & probing interval T probe –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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11 Inspired by Bettstetter et al. ’ s work –For any mobility model (random walk, random way point), Bettstetter et al. have shown that 1 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)
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