Rational Adversary Research Articles

How to Make Rational Arguments Practical and Extractable

We investigate proof systems where security holds against rational parties instead of malicious ones. Our starting point is the notion of rational arguments, a variant of rational proofs (Azar and Micali, STOC 2012) where security holds against rational adversaries that are also computationally bounded. Rational arguments are an interesting primitive because they generally allow for very efficient protocols, and in particular sublinear verification (i.e. where the Verifier does not have to read the entire input). In this paper we aim at narrowing the gap between literature on rational schemes and real world applications. Our contribution is two-fold. We provide the first construction of rational arguments for the class of polynomial computations that is practical (i.e., it can be applied to real-world computations on reasonably common hardware) and with logarithmic communication. Techniques-wise, we obtain this result through a compiler from information-theoretic protocols and rational proofs for polynomial evaluation. The latter could be of independent interest. As a second contribution, we propose a new notion of extractability for rational arguments. Through this notion we can obtain arguments where knowledge of a witness is incentivized (rather than incentivizing mere soundness). We show how our aforementioned compiler can also be applied to obtain efficient extractable rational arguments for N P .

Securing blockchain-based timed data release against adversarial attacks1

Timed data release refers to protecting sensitive data that can be accessed only after a pre-determined amount of time has passed. While blockchain-based solutions for timed data release provide a promising approach for decentralizing the process, designing an attack-resilient timed-release service that is resilient to malicious adversaries in a blockchain network is inherently challenging. A timed-release service on a blockchain network is inevitably exposed to the risk of post-facto attacks where adversaries may launch attacks after the data is released in the blockchain network. Existing incentive-based solutions for timed data release in Ethereum blockchains guarantee protection under the assumption of a fully rational adversarial environment in which every peer acts rationally. However, these schemes fail invariably when even a single participating peer node in the protocol starts acting maliciously and deviates from the rational behavior. In this paper, we propose a systematic solution for attack-resilient and practical blockchain-based timed data release in a mixed adversarial environment, where both malicious adversaries and rational adversaries exist. We first propose an effective uncertainty-aware reputation measure to capture the behaviors of the peer involved in timed data release activities in the network. In light of such a measure, we present the design of a basic protocol that consists of two critical ingredients, namely reputation-aware peer recruitment and verifiable enforcement protocols. The former, prior to the start of the enforcement protocols, performs peer recruitment based on the reputation measure to make the design probabilistically attack-resilient to the post-facto attacks. The latter is responsible for contractually guarding the recruited peers at runtime by transparently reporting observed adversarial behaviors. However, the basic recruitment design is only aware of the reputation of the peers and it does not consider the working time schedule of the participating peers and as a result, it results in lower attack-resilience. To enhance the attack resilience further without impacting the verifiable enforcement protocols, we propose a temporal graph-based reputation-aware peer recruitment algorithm that carefully determines the peer recruitment plan to make the service more attack-resilient. In our proposed approach, we formally capture the timed data release service as a temporal graph and we develop a novel maximal attack-resilient path-finding algorithm on the temporal graph for the participating peers. We implement a prototype of the proposed approach using Smart Contracts and deploy it on the Ethereum official test network, Rinkeby. For extensively evaluating the proposed techniques, we perform simulation experiments to validate the effectiveness of the reputation-aware timed data release protocols as well as our proposed temporal-graph-based improvements. The results demonstrate the effectiveness and strong attack resilience of the proposed mechanisms and our approach incurs only a modest gas cost.

Choices Are Not Independent: Stackelberg Security Games with Nested Quantal Response Models

The quantal response (QR) model is widely used in Stackelberg security games (SSG) to model a bounded rational adversary. The QR model is a model of human response from among a large variety of prominent models known as discrete choice models. QR is the simplest type of discrete choice models and does not capture commonly observed phenomenon such as correlation among choices. We introduce the nested QR adversary model (based on nested logit model in discrete choice theory) in SSG which addresses shortcoming of the QR model. We present tractable approximation of the resulting equilibrium problem with nested QR adversary. We do so by deriving an interesting property of the equilibrium problem, namely a loosely coupled split into nested problems that mirrors the nested decision making by the adversary in the nested QR model. We show that each separate nested problem can be approximated efficiently and that the loosely coupled overall problem can be solved approximately by formulating it as a discretized version of a continuous dynamic program. Finally, we conduct experiments that show the scalability and parallelizability of our approach, as well as advantages of the nested QR model.

Perfectly Secure Message Transmission Against Rational Adversaries

Secure Message Transmission (SMT) is a two-party cryptographic protocol by which the sender can securely and reliably transmit messages to the receiver using multiple channels. An adversary can corrupt a subset of the channels and commit eavesdropping and tampering attacks over the channels. In this work, we introduce a game-theoretic security model for SMT in which adversaries have some preferences for protocol execution. We define rational “timid” adversaries who prefer to violate security requirements but do not prefer the tampering to be detected. First, we consider the basic setting where a single adversary attacks the protocol. We construct perfect SMT protocols against any rational adversary corrupting all but one of the channels. Since minority corruption is required in the traditional setting, our results demonstrate a way of circumventing the cryptographic impossibility results by a game-theoretic approach. Next, we study the setting in which all the channels can be corrupted by multiple adversaries who do not cooperate. Since we cannot hope for any security if a single adversary corrupts all the channels or multiple adversaries cooperate maliciously, the scenario can arise from a game-theoretic model. We also study the scenario in which both malicious and rational adversaries exist.

Towards a Cognitive Theory of Cyber Deception.

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.

Cyber-threat perception and risk management in the Swedish financial sector

The financial sector relies heavily on information systems for business. This study sets out to investigate cyber situation awareness in the financial sector in Sweden, by examining what information elements that are needed for a common operational picture, and exploring how key actors perceive cyber-threats.Data was collected through a survey and a series of interviews with key actors in the sector in conjunction with a national level crisis management exercise. The data was then analyzed and contrasted to theory. Conclusions were drawn and results discussed. Finally, possible mitigation actions were suggested.It was found that actors in the Swedish financial sector have a well developed crisis management working concept. However, information about rational adversaries that cause prolonged disturbances is possibly not collected, analyzed and utilized systematically. Much effort is put into ensuring that timely and relevant information from organizations is shared in an efficient manner. The sector perceives cyber-threats against the underlying financial infrastructure, as well as against IT service availability and data confidentiality, besides financial theft. The sector has particular concerns for the potential of reputational loss due to cyberattacks. There are also special concerns about the insider threat.Respondents agree that risk management has to account for cyber risk. A possible route to enhance risk management practices is to ensure that cyber personnel is integrated in crisis management teams.

Proofs of Data Reliability: Verification of reliable data storage with automatic maintenance†

AbstractProofs of Data Reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose two novel Proof of Data Reliability schemes, namely POROS and PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. As a result, POROS and PORTOS are compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. Moreover, POROS and PORTOS delegate the burden of generating the redundancy to the cloud as well. The security of both solutions is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost.

Gray Hole Attack as a Byzantine Attack in a Wireless Multi-Hop Network

Gray hole attack gets trickier, when only certain node’s packets are dropped intentionally, termed as the Selective Gray Hole Attack (SGHA). The literature so far has focused on the attack carried out on random packets, without any particular criterion. SGHA has enough potential to disrupt the communication of a particular node with the outside world or confine the communication of a network within itself. This could be detrimental in military reconnaissance applications, disaster warning systems, autonomous cars, e-health systems, home automation, etc. Hence, we propose a Quality of Service (QoS) aware algorithm to detect and mitigate such attacks, performed by a rational adversary.

Rational adversary with flexible utility in secure two-party computation

Secure two-party computation evaluates a function among two distributed parties without revealing the parties’ inputs except for the function’s outputs. Secure two-party computation can be applied into various fields like cloud computing, which is a composition of distribute computing, parallel computing and utility computing etc. Rational secure two-party computation may achieve some desirable properties under two assumptions deriving from STOC 2004. However, the emergence of new computing paradigms like pay-as-you-go model restricts the application of rational protocols. Previous adversaries does not consider payment in secure two-party protocols. Therefore, new type of adversaries should be propose for these new paradigms. In this paper, we address this problem by proposing a new kind of rational adversary, who consider payment in his relaxed utilities. The utilities are based on economic incentives instead of standard assumptions. Furthermore, the new rational adversary is assumed to negotiate with rational parties in protocols. It’s similar to “cost corruption” but more flexible. Our new adversary can dynamically negotiate with each rational party in different phases in order to maximize his utilities. To verify the validity of the new adversary, we model a rational secure two-party protocol, which inherits the hybrid framework of STOC 2007. We also prove the security in the presence of the new rational adversary under ideal/real paradigm.

Comparing human behavior models in repeated Stackelberg security games: An extended study

Several competing human behavior models have been proposed to model boundedly rational adversaries in repeated Stackelberg Security Games (SSG). However, these existing models fail to address three main issues which are detrimental to defender performance. First, while they attempt to learn adversary behavior models from adversaries' past actions (“attacks on targets”), they fail to take into account adversaries' future adaptation based on successes or failures of these past actions. Second, existing algorithms fail to learn a reliable model of the adversary unless there exists sufficient data collected by exposing enough of the attack surface – a situation that often arises in initial rounds of the repeated SSG. Third, current leading models have failed to include probability weighting functions, even though it is well known that human beings' weighting of probability is typically nonlinear.To address these limitations of existing models, this article provides three main contributions. Our first contribution is a new human behavior model, SHARP, which mitigates these three limitations as follows: (i) SHARP reasons based on success or failure of the adversary's past actions on exposed portions of the attack surface to model adversary adaptivity; (ii) SHARP reasons about similarity between exposed and unexposed areas of the attack surface, and also incorporates a discounting parameter to mitigate adversary's lack of exposure to enough of the attack surface; and (iii) SHARP integrates a non-linear probability weighting function to capture the adversary's true weighting of probability. Our second contribution is a first “repeated measures study” – at least in the context of SSGs – of competing human behavior models. This study, where each experiment lasted a period of multiple weeks with individual sets of human subjects on the Amazon Mechanical Turk platform, illustrates the strengths and weaknesses of different models and shows the advantages of SHARP. Our third major contribution is to demonstrate SHARP's superiority by conducting real-world human subjects experiments at the Bukit Barisan Seletan National Park in Indonesia against wildlife security experts.

War with Crazy Types

This article introduces a model of war and peace in which leaders believe that their adversaries might be crazy types who always behave aggressively, regardless of whether it is strategically optimal to do so. In the model, two countries are involved in a dispute that can either end in a peaceful settlement or escalate into ‘limited war’ or ‘total war.’ If it is common knowledge that the leaders of the countries are strategically rational, then the only equilibrium outcome of the model is peace. Yet if a leader believes that there is a chance that her adversary is a crazy type, then even a strategically rational adversary may have an incentive to adopt a madman strategy in which he pretends to be crazy. This leads to limited war with positive probability, even when both leaders are strategically rational. The article shows that despite having two-sided incomplete information, the model has a generically unique equilibrium. Moreover, the model identifies two countervailing forces that drive equilibrium behavior: a reputation motive and a defense motive. When the prior probability that a leader is crazy decreases, the reputation motive promotes less aggressive behavior by that leader, while the defense motive pushes for more aggressive behavior. These two forces underlay several comparative statics results. For example, the study analyzes the effect of increasing the prior probability that a leader is crazy, and the effect of changing the relative military strengths of the countries, on the equilibrium behavior of both leaders. The analysis also characterizes the conditions under which the madman strategy is profitable (or not), which contributes to the debate in the literature about its effectiveness.

Secure Set Intersection in the Presence of a Rational Adversary

Secure set intersection protocols confronting with covert adversary are efficient and guarantee correctness with a non-negligible probability, which is smaller than 1. However, they are not desirable when correctness is required to be achieved with probability 1, since correctness may be broken when the covert adversary successfully cheats. In order to get desirable result about correctness, we introduce a rational adversary instead of a covert adversary into the secure set-intersection protocol using smartcard tokens. The rational adversary has the same “deterrence factor” as a covert adversary but additionally has utilities defined as in the game theory. The main target of the rational adversary is to maximize his utilities by cheating in the protocol. Note that cheating does not bring him optimal utility, thus he has no incentives to cheat. We prove that given proper utilities, the strategy not to cheat induces Nash equilibrium for the rational adversary. Therefore, correctness can be guaranteed with probability 1. Furthermore, our protocol has the same round complexity as the secure setintersection protocol using smartcard tokens since the introduction of a rational adversary does not increase the round complexity.

New rational parties relying on reputation

In this paper, we redefine the utilities considering other parts such as reputation in order to give rational parties incentives to participate in the protocol and finally get the correct outputs. The most distinct contribution of this paper is the proposal of rational covert adversaries whose behaviors are combinations of rational parties and covert adversaries. Finally, given proper parameters, we prove that all parties have incentives to participate in the rational secure computation protocol. Copyright © 2013 John Wiley & Sons, Ltd.

A stochastic games framework for verification and control of discrete time stochastic hybrid systems

We describe a framework for analyzing probabilistic reachability and safety problems for discrete time stochastic hybrid systems within a dynamic games setting. In particular, we consider finite horizon zero-sum stochastic games in which a control has the objective of reaching a target set while avoiding an unsafe set in the hybrid state space, and a rational adversary has the opposing objective. We derive an algorithm for computing the maximal probability of achieving the control objective, subject to the worst-case adversary behavior. From this algorithm, sufficient conditions of optimality are also derived for the synthesis of optimal control policies and worst-case disturbance strategies. These results are then specialized to the safety problem, in which the control objective is to remain within a safe set. We illustrate our modeling framework and computational approach using both a tutorial example with jump Markov dynamics and a practical application in the domain of air traffic management.

Improving resource allocation strategies against human adversaries in security games: An extended study

Stackelberg games have garnered significant attention in recent years given their deployment for real world security. Most of these systems, such as ARMOR, IRIS and GUARDS have adopted the standard game-theoretical assumption that adversaries are perfectly rational, which is standard in the game theory literature. This assumption may not hold in real-world security problems due to the bounded rationality of human adversaries, which could potentially reduce the effectiveness of these systems.In this paper, we focus on relaxing the unrealistic assumption of perfectly rational adversary in Stackelberg security games. In particular, we present new mathematical models of human adversariesʼ behavior, based on using two fundamental theory/method in human decision making: Prospect Theory (PT) and stochastic discrete choice model. We also provide methods for tuning the parameters of these new models. Additionally, we propose a modification of the standard quantal response based model inspired by rank-dependent expected utility theory. We then develop efficient algorithms to compute the best response of the security forces when playing against the different models of adversaries. In order to evaluate the effectiveness of the new models, we conduct comprehensive experiments with human subjects using a web-based game, comparing them with models previously proposed in the literature to address the perfect rationality assumption on part of the adversary.Our experimental results show that the subjectsʼ responses follow the assumptions of our new models more closely than the previous perfect rationality assumption. We also show that the defender strategy produced by our new stochastic discrete choice model outperform the previous leading contender for relaxing the assumption of perfect rationality. Furthermore, in a separate set of experiments, we show the benefits of our modified stochastic model (QRRU) over the standard model (QR).11This paper significantly extends our previous conference paper (Yang et al., 2011) [1] by providing (i) new methods for setting parameters of the Prospect Theory model; (ii) an additional variant of Quantal Response model and a new algorithm to compute defender strategies against the new model; (iii) a more comprehensive set of experiments which includes multiple new algorithms and updated settings for the algorithms; (iv) new analysis of the robustness of different defender strategies and the predictive accuracy of different models; (v) additional discussion of related work.

Rational Adversaries? Evidence from Randomised Trials in One Day Cricket

In cricket, the right to make an important strategic decision ( bat first or field first) is assigned via a coin toss. I use these 'randomised trials' to examine the consistency of choices made by teams with strictly opposed preferences and the effects of these choices upon the outcomes in the game. I find significant evidence of inconsistency, with teams often agreeing on who is to bat first. Estimated treatment effects show that choices are often poorly made and reduce the probability of the team winning, a particularly surprising finding given the intensely competitive environment and opportunities for learning.

A Rule-Based and Game-Theoretic Approach to Online Credit Card Fraud Detection

Traditional security mechanisms are often found to be inadequate for protection against attacks by authorized users or intruders posing as authorized users. This has drawn the interest of the research community towards intrusion detection techniques. We model the conflicting motives between an intruder and an intrusion detection system as a multistage game between two players, each trying to maximize its payoff. We consider the specific application of credit card fraud detection and propose a two-tiered architecture having a rule-based component in the first tier and a game-theoretic component in the second tier. Classical game theory is considered useful in many situations because it permits the formulation of strategies that are optimal, regardless of what the adversary does, negating the need for prediction of his/her behavior. However, we use it in a predictive application in the sense that we consider intruders as rational adversaries who would try to behave optimally, and the expected optimal behavior can be determined through game theory.

Learning about a population of agents and the evolution of trust and cooperation

Various reputation models consider how cooperation might emerge between rational adversaries in non-cooperative games. We suggest and analyze an additional determinant: uncertainty and learning about the population from which agents are selected. The analysis shows how the degrees of trust by principals and cooperation by agents can depend on the past behavior of other agents. A dramatic implication is that trust and cooperation can permanently and inefficiently break down due to revised beliefs about the population of agents. We consider lessons the theory might provide concerning the relationship between the U.S. Department of Defense and its contractors. The model suggests two explanations of events during the mid-1980s, which many interpret as a very socially costly breakdown of trust and cooperation.