A Secure Ad-hoc Routing Approach using Localized Self-healing Communities MobiHoc, 2005 Presented by An Dong-hyeok CNLAB at KAIST

Community-based secure routing protocol Analytic model Simulation Contents Introduction Problem statement Community-based secure routing protocol Analytic model Simulation Conclusions 2 CNLAB at KAIST CALAB at KAIST

1. Introduction Introduction Mobile ad hoc networks(MANETs) Vulnerable to routing attacks( especially attacks launched by non-cooperative network members ) Packet loss is common Security threats about routing have not been fully addressed 3 A new intrusion protection mechanism, community-based security Suggest the “self-healing community” From node-to-node delivery to community-to-community Solution CNLAB at KAIST

2. Problem statement Benefits 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 4 Indirect RREQ floods RREP & DATA packet loss Indirectly trigger more RREQ floods Excessive floods deplete network resource CNLAB at KAIST

dest source RREQ RREP 2. Problem statement (Indirect attack example) Benefits 2. Problem statement (Indirect attack example) RREQ 5 dest source RREP RREQ forwarding 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? CNLAB at KAIST

3. Community-based secure routing protocol Technology 3. Community-based secure routing protocol 3.1 Network assumptions Assumption 1 A node can always monitor ongoing transmissions even if the node itself is not the intended receiver 6 Assumption 2 Radio transmission is omni-directional and radio links are symmetric Assumption 3 In a network locality there are redundant network members with high probability CNLAB at KAIST

3. Community-based secure routing protocol Technology 3. Community-based secure routing protocol 3.2 Network security assumptions Assumption 1 All packet transmissions (including control, data packets and their ACKs) are protected by data origin authentication service. Every packet is authenticated and the packet sender’s identity is unforgeable 7 Assumption 2 The ad hoc nodes are equipped with hardware needed by packet leashes or Brands-Chaum protocols[6] Any pair of topological neighbors in ad hoc routing are physical neighbors CNLAB at KAIST

3. Community-based secure routing protocol Technology 3. Community-based secure routing protocol 3.3 Self-healing 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 8 Community leadership is determined by contribution Leader steps down (being taken over) if not doing its job (doesn’t forward within a timeout) Community member Community member must be in the transmission range of exactly three RREP forwarders CNLAB at KAIST

B C D Community 3. Community-based secure routing protocol Technology 3. Community-based secure routing protocol 3.3 Self-healing community (2-hop scenario) Community 9 B C D CNLAB at KAIST

dest source Communities 3. Community-based secure routing protocol Technology 3. Community-based secure routing protocol 3.4 Self-healing community (multi-hop scenario) Communities source dest 1010 The concept of “self-healing community” is applicable to multi-hop routing CNLAB at KAIST

Community around V formed upon hearing RREP Technology 3. Community-based secure routing protocol 3.4 on-demand initial configuration Community around V formed upon hearing RREP RREQ upstream 1111 V1 U V E V2 RREP EV CNLAB at KAIST

Communities (if C forwards a correct RREP) Technology 3. Community-based secure routing protocol Communities (if C forwards a correct RREP) C” 1212 Communities(C’ wins) D E B C dest source C’ CNLAB at KAIST

source dest PROBE PROBE_REP X no ACK Technology 3. Community-based secure routing protocol 3.4 reconfiguration of self-healing community (multi-hop scenario) PROBE PROBE_REP 1313 source X no ACK dest CNLAB at KAIST

4. Analytic model 4.1 mobile network model Technology Divides the network into large number n of very small tiles A node’s presence probability P at each tile is small A spatial binomial distribution B(n, p) 14 When n is large and P is small, B(n, p) is approximately a spatial Poisson distribution with rate If there are N mobile nodes roaming i.i.d The probability of exactly k nodes in an area A’ CNLAB at KAIST

4. Analytic model 4.2 Community area Aheal Technology 15 (left) maximal community 2-hop RREP nodes are Area approaching (right) minimal community 2-hop RREP nodes are Area approaching 0 CNLAB at KAIST

4. Analytic model 4.3 modeling adversarial presence Technology Θ: percentage of non-cooperative network members X: number of nodes in the forwarding community area 16 Y: number of cooperative nodes Z: number of non-cooperative nodes CNLAB at KAIST

4. Analytic model 4.4 Effectiveness of CBS routing Technology Per-hop failure prob. Of community-to-community routing is negligible with respect to network scale N 17 Per-hop success prob. Of node-to-node ad hoc routing schemes is negligible Tremendous gain EG := 1 / negligible CNLAB at KAIST

Technology 4. Analytic model 4.4 Effectiveness of CBS routing N q 18 It is even more tremendous when either network scale or non-cooperative ratio increases. CNLAB at KAIST

Alternative 4. Simulation 4.1 Performance Gap 19 CBS-AODV’s performance only drops slightly with more non-cooperative behavior CNLAB at KAIST

Alternative 4. Simulation 4.1 Mobility’s impact 20 CNLAB at KAIST

Alternative 4. Simulation 4.1 Less RREQ 21 In CBS-AODV, # of RREQ triggered is less sensitive to non-coorperative ratio CNLAB at KAIST

4. Conclusions Conclusion Conventional node-to-node routing is vulnerable to routing disruptions Excessive but protocol-compliant RREQ floods RREP / DATA packet loss 22 Analytic study approves the community design The new community-to-community secure routing is solution More optimal estimation of forwarding window & probing interval Secure and efficient key management between two communities Open challenges CNLAB at KAIST

23 Any Question? CNLAB at KAIST