TY - GEN
T1 - An Evolutionary Game model for threat revocation in ephemeral networks
AU - Abass, Ahmed A.Alabdel
AU - Mandayam, Narayan B.
AU - Gajic, Zoran
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/10
Y1 - 2017/5/10
N2 - We consider a wireless network of M nodes connected together in a decentralized way (for example as an ad hoc network), and according to pre-specified rules. There are other malicious node(s) which can be either inserted or infected which are trying to disturb the operation of the network. The nodes are cooperating to defend the network (and eventually themselves) by isolating the misbehaved node(s). We approach this problem using Evolutionary Game Theory (EGT), and characterize the robust equilibrium point(s) for this game. The game is formulated such that all the nodes take part in the decision process to avoid problems caused by unsuccessful revocation or over reacted revocation decisions. Each node in the network (interchangeably called benign node to distinguish it from the malicious node or the intruder) has three decisions to make: (a) abstain or do nothing; (b) self-sacrifice by disconnecting the intruder and itself; and (c) voting to isolate the intruding node. Each decision has its advantages and disadvantages and the Replicator Dynamics (RD) is used to show the dynamics of the nodes' decisions. By simulating the RD equation, two different cases emerge as Evolutionary Stable Strategies (ESS) where one of them is the desired ESS, and the other is not. Phase portrait diagrams are used to characterize the fraction of the M nodes needed to choose each one of these ESS's, the rate of convergence, and the effect of increasing the cooperation rewards.
AB - We consider a wireless network of M nodes connected together in a decentralized way (for example as an ad hoc network), and according to pre-specified rules. There are other malicious node(s) which can be either inserted or infected which are trying to disturb the operation of the network. The nodes are cooperating to defend the network (and eventually themselves) by isolating the misbehaved node(s). We approach this problem using Evolutionary Game Theory (EGT), and characterize the robust equilibrium point(s) for this game. The game is formulated such that all the nodes take part in the decision process to avoid problems caused by unsuccessful revocation or over reacted revocation decisions. Each node in the network (interchangeably called benign node to distinguish it from the malicious node or the intruder) has three decisions to make: (a) abstain or do nothing; (b) self-sacrifice by disconnecting the intruder and itself; and (c) voting to isolate the intruding node. Each decision has its advantages and disadvantages and the Replicator Dynamics (RD) is used to show the dynamics of the nodes' decisions. By simulating the RD equation, two different cases emerge as Evolutionary Stable Strategies (ESS) where one of them is the desired ESS, and the other is not. Phase portrait diagrams are used to characterize the fraction of the M nodes needed to choose each one of these ESS's, the rate of convergence, and the effect of increasing the cooperation rewards.
KW - Ephemeral Networks
KW - Evolutionary Game Theory
KW - Replicator Dynamics
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85020185871&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020185871&partnerID=8YFLogxK
U2 - 10.1109/CISS.2017.7926128
DO - 10.1109/CISS.2017.7926128
M3 - Conference contribution
AN - SCOPUS:85020185871
T3 - 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
BT - 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Annual Conference on Information Sciences and Systems, CISS 2017
Y2 - 22 March 2017 through 24 March 2017
ER -