TY - GEN
T1 - Using signaling games to model the multi-step attack-defense scenarios on confidentiality
AU - Lin, Jingqiang
AU - Liu, Peng
AU - Jing, Jiwu
PY - 2012
Y1 - 2012
N2 - In the multi-step attack-defense scenarios (MSADSs), each rational player (the attacker or the defender) tries to maximize his payoff, but the uncertainty about his opponent prevents him from taking the suitable actions. The defender doesn't know the attacker's target list, and may deploy unnecessary but costly defenses to protect machines not in the target list. Similarly, the attacker doesn't know the deployed protections, and may spend lots of time and effort on a well-protected machine. We develop a repeated two-way signaling game to model the MSADSs on confidentiality, and show how to find the actions maximizing the expected payoffs through the equilibrium. In the proposed model, on receiving each intrusion detection system alert (i.e., a signal), the defender follows the equilibrium to gradually reduce the uncertainty about the attacker's targets and calculate the defenses maximizing his expected payoff.
AB - In the multi-step attack-defense scenarios (MSADSs), each rational player (the attacker or the defender) tries to maximize his payoff, but the uncertainty about his opponent prevents him from taking the suitable actions. The defender doesn't know the attacker's target list, and may deploy unnecessary but costly defenses to protect machines not in the target list. Similarly, the attacker doesn't know the deployed protections, and may spend lots of time and effort on a well-protected machine. We develop a repeated two-way signaling game to model the MSADSs on confidentiality, and show how to find the actions maximizing the expected payoffs through the equilibrium. In the proposed model, on receiving each intrusion detection system alert (i.e., a signal), the defender follows the equilibrium to gradually reduce the uncertainty about the attacker's targets and calculate the defenses maximizing his expected payoff.
UR - http://www.scopus.com/inward/record.url?scp=84869465454&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-34266-0_7
DO - 10.1007/978-3-642-34266-0_7
M3 - Conference contribution
AN - SCOPUS:84869465454
SN - 9783642342653
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 118
EP - 137
BT - Decision and Game Theory for Security - Third International Conference, GameSec 2012, Proceedings
T2 - 3rd International Conference on Decision and Game Theory for Security, GameSec 2012
Y2 - 5 November 2012 through 6 November 2012
ER -