301 research outputs found
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
Towards a science of security games
Abstract. Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the impor-tance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Com-putational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of secu-rity resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games”. The research challenges posed by these applications include scaling up security games to real-world sized prob-lems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries.
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the
success of many non-cooperative multi-agent applications. However, in many
real-world situations, we may face the exact opposite of this game-theoretic
problem -- instead of prescribing equilibrium of a given game, we may directly
observe the agents' equilibrium behaviors but want to infer the underlying
parameters of an unknown game. This research question, also known as inverse
game theory, has been studied in multiple recent works in the context of
Stackelberg games. Unfortunately, existing works exhibit quite negative
results, showing statistical hardness and computational hardness, assuming
follower's perfectly rational behaviors. Our work relaxes the perfect
rationality agent assumption to the classic quantal response model, a more
realistic behavior model of bounded rationality. Interestingly, we show that
the smooth property brought by such bounded rationality model actually leads to
provably more efficient learning of the follower utility parameters in general
Stackelberg games. Systematic empirical experiments on synthesized games
confirm our theoretical results and further suggest its robustness beyond the
strict quantal response model
Improved Intrusion Detection System using Quantal Response Equilibrium-based Game Model and Rule-based Classification
Wireless sensor network has large number of low-cost tiny nodes with sensing capability. These provide low cost solutions to many real world problems such as such as defence, Internet of things, healthcare, environment monitoring and so on. The sensor nodes of these networks are placed in vulnerable environment. Hence, the security of these networks is very important. Intrusion Detection System (IDS) plays an important role in providing a security to such type of networks. The sensor nodes of the network have limited power and, traditional security mechanisms such as key-management, encryption decryption and authentication techniques cannot be installed on the nodes. Hence, there is a need of special security mechanism to handle the intrusions. In this paper, intrusion detection system is designed and implemented using game theory and machine learning to identify multiple attacks. Game theory is designed and used to apply the IDS optimally in WSN. The game model is designed by defining the players and the corresponding strategies. Quantal Response Equilibrium (QRE) concept of game theory is used to select the strategies in optimal way for the intrusion’s detection. Further, these intrusions are classified as denial of service attack, rank attack or selective forwarding attacks using supervised machine learning technique based on different parameters and rules. Results show that all the attacks are detected with good detection rate and the proposed approach provides optimal usage of IDS
End-to-End Game-Focused Learning of Adversary Behavior in Security Games
Stackelberg security games are a critical tool for maximizing the utility of
limited defense resources to protect important targets from an intelligent
adversary. Motivated by green security, where the defender may only observe an
adversary's response to defense on a limited set of targets, we study the
problem of learning a defense that generalizes well to a new set of targets
with novel feature values and combinations. Traditionally, this problem has
been addressed via a two-stage approach where an adversary model is trained to
maximize predictive accuracy without considering the defender's optimization
problem. We develop an end-to-end game-focused approach, where the adversary
model is trained to maximize a surrogate for the defender's expected utility.
We show both in theory and experimental results that our game-focused approach
achieves higher defender expected utility than the two-stage alternative when
there is limited data.Comment: Appeared at AAAI 202
Maximising social welfare in selfish multi-modal routing using strategic information design for quantal response travelers
Traditional selfish routing literature quantifies inefficiency in transportation systems with single-attribute costs using price-of-anarchy (PoA), and provides various technical approaches (e.g. marginal cost pricing) to improve PoA of the overall network. Unfortunately, practical transportation systems have dynamic, multi-attribute costs and the state-of-the-art technical approaches proposed in the literature are infeasible for practical deployment. In this paper, we offer a paradigm shift to selfish routing via characterizing idiosyncratic, multiattribute costs at boundedly-rational travelers, as well as improving network efficiency using strategic information design. Specifically, we model the interaction between the system and travelers as a Stackelberg game, where travelers adopt multi-attribute logit responses. We model the strategic information design as an optimization problem, and develop a novel approximate algorithm to steer Logit Response travelers towards social welfare using strategic Information design (in short, LoRI). We tested the performance of LoRI and compare with that of a SSSP algorithm on a Wheatstone network with multi-modal routes. We improved LoRI and demonstrated the enhanced performance of LoRI V2 when compared to LoRI V1 in similar experiment settings. We considered a portion of Manhattan, New York, USA and presented the performance of LoRI on a real world multi modal transportation network. In all our simulation experiments, including real world networks, we find that LoRI outperforms traditional state of the art routing algorithms, in terms of system utility, and reduces the cost at travelers when large number of travelers on the network interact with LoRI --Abstract, page iii
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