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Security Issues in Ant Routing Weilin Zhong Outline Swarm Intelligence AntNet Routing Algorithm Security Issues in AntNet Possible Solutions Swarm Intelligence Biology Swarms Social Insect Characteristics Complex Group Behaviors Simple Individual Behaviors Local Communication and Interaction No Leader or Coordinator Swarm Intelligence Complex Group Behaviors Nest Building Wasp Nets Swarm Intelligence Complex Group Behavior - Ant Forage Model Swarm Intelligence How can simple individuals achieve complex group behaviors? --The Intelligence of biological swarm lies on its interaction network Swarm Intelligence Two Kinds of Interactions Locally Direct Interaction A member interacts with its neighbors Indirection Interaction -- Stigmergy Interaction intermediate by the common environment Swarm Computing Model Computing Swarm Computing Swarm is a collection of large number of small computational members Examples: Sensor Networks, Multi-agent System, Amorphous Computer System Characteristics Myriad numbers of elements. Decentralized control Autonomous members Sophisticated global behaviors Adaptive and self-organized AntNet Routing Algorithm Swarm Intelligence In General Network Routing Problems Learn from the Ant Forage Model AntNet Routing Algorithm A Distributed Adaptive Routing Algorithm Mobile Multi-agent Systems Inspired by the stigmergy model in ant colonies AntNet Routing - Agents Two kinds of Agents(Ant Packets) Forward Ant explores the network and collects information when reaches the destination, changes into backward ant Backward Ant goes back in the same path as forward ant update routing tables for all the nodes in the path AntNet Routing --- Agents Agents behaviors (N1,T1) (N2,T2) (N1,T1) Forward Ant 1 Backward Ant (N1, T1) Forward Ant 2 Backward Ant (N1,T1) (N2,T2) (N2,T2) (N3,T3) (N3,T3) Forward Ant (N4, T4) Forward Ant 3 Backward Ant 4 Backward Ant AntNet Routing- Router Probabilistic Routing Table Port/ N1 dest 1 P11 N2 … Nn P12 … P1n 2 P21 P22 … P2n 3 P31 P32 … P3n 1 1 3 Local Statistics Model N1 Stat1 N2 Stat2 … … 2 Nn Statn AntNet Routing - Router AntNet Router Structure Forward_ANT Data Packet Lookup and forwarding P\N 1 2 … 3 Backward_ANT N1 P11 P21 … P31 N2 P12 P22 … P32 … … … … … Updating N3 P13 P23 … P33 Out[ ] Buffer Port1 FIFO1 Schedule r FIFO2 Port2 FIFO1 Schedule r FIFO2 Port N FIFO1 FIFO2 Schedule r AntNet Routing Measure Trip Time (N2,T2) (N1,T1) Forward Ant 1 Backward Ant (N1, T1) Forward Ant 2 Backward Ant (N1,T1) (N1,T1) (N2,T2) (N2,T2) (N3,T3) (N3,T3) Forward Ant (N4, T4) Forward Ant 3 Backward Ant VT1 = VT2 VT2 = VT3 VT3 = VT4 + Local_VT + Local_VT + Local_VT 4 Backward Ant VT4 = 0 Local_VT = Channel_Tran_Prop_delay + Load_Dependent_Processing_Delay AntNet Routing Algorithms Updating Routing Table Reinforcement Factor r = C1 * ( Wbest/T) + C2 * Var Increase the probability of the channel that backward ant comes from P’ = P + r * (1 - P) Decrease the probability of the other channels P’ = P * ( 1 - r) AntNet Routing Algorithm A Simple Network Example 1 0 1 3 2 0 0 0 4 1 1 0 1 1 2 3 2 2 0 Security Issues in AntNet Threats untrustworthy hosts Malicious Agents Attack Goals increase the packet latency --- mislead packets to a longer path Break down a critical node --- mislead packets to a certain node to overload it Drop Data Packets --- mislead packets to a malicious node Security Issues Untrustworthy Hosts forward data packet to a wrong direction delay data packet generate a bursts of ant packets to reinforce bad path generate false ant packets to make good path bad Malicious Agents carried false trip time information Possible Solutions Authentication Mechanism problem: expensive Time Stamp or Sequence Number on Agent Better Adaptive Model for quick recovery form the impact of malicious agent Other solutions?...