Stackelberg vs. Nash in Security Games: An Extended Investigation of Interchangeability, Equivalence, and Uniqueness

Zero-determinant strategy in stochastic Stackelberg asymmetric security game

In a stochastic Stackelberg asymmetric security game, the strong Stackelberg equilibrium (SSE) strategy is a popular option for the defender to get the highest utility against an attacker with the best response (BR) strategy. However, the attacker may be a boundedly rational player, who adopts a combination of the BR strategy and a fixed stubborn one. In such a condition, the SSE strategy may not maintain the defensive performance due to the stubbornness. In this paper, we focus on how the defender can adopt the unilateral-control zero-determinate (ZD) strategy to confront the boundedly rational attacker. At first, we verify the existence of ZD strategies for the defender. We then investigate the performance of the defender's ZD strategy against a boundedly rational attacker, with a comparison of the SSE strategy. Specifically, when the attacker's strategy is close to the BR strategy, the ZD strategy admits a bounded loss for the defender compared with the SSE strategy. Conversely, when the attacker's strategy is close to the stubborn strategy, the ZD strategy can bring higher defensive performance for the defender than the SSE strategy does.

Applying the Bayesian Stackelberg Active Deception Game for Securing Infrastructure Networks

With new security threats cropping up every day, finding a real-time and smart protection strategy for critical infrastructure has become a big challenge. Game theory is suitable for solving this problem, for it provides a theoretical framework for analyzing the intelligent decisions from both attackers and defenders. However, existing methods are only based on complete information and only consider a single type of attacker, which is not always available in realistic situations. Furthermore, although infrastructure interconnection has been greatly improved, there is a lack of methods considering network characteristics. To overcome these limitations, we focus on the problem of infrastructure network protection under asymmetry information. We present a novel method to measure the performance of infrastructure from the network perspective. Moreover, we propose a false network construction method to simulate how the defender applies asymmetric information to defend against the attacker actively. Meanwhile, we consider multiple types of attackers and introduce the Bayesian Stackelberg game to build the model. Experiments in real infrastructure networks reveal that our approach can improve infrastructure protection performance. Our method gives a brand new way to approach the problem of infrastructure security defense.

A Game-Theoretic Framework to Preserve Location Information Privacy in Location-based Service Applications

Recently, the growing ubiquity of location-based service (LBS) technology has increased the likelihood of users' privacy breaches due to the exposure of their real-life information to untrusted third parties. Extensive use of such LBS applications allows untrusted third-party adversarial entities to collect large quantities of information regarding users' locations over time, along with their identities. Due to the high risk of private information leakage using resource-constrained smart mobile devices, most LBS users may not be adequately encouraged to access all LBS applications. In this paper, we study the use of game theory to protect users against private information leakage in LBSs due to malicious or selfish behavior of third-party observers. In this study, we model a scenario of privacy protection gameplay between a privacy protector and an outside visitor and then derive the situation of the prisoner's dilemma game to analyze the traditional privacy protection problems. Based on the analysis, we determine the corresponding benefits to both players using a point of view that allows the visitor to access a certain amount of information and denies further access to the user's private information when exposure of privacy is forthcoming. Our proposed model uses the collection of private information about historical access data and current LBS access scenario to effectively determine the probability that the visitor's access is an honest one. Moreover, we present the procedures involved in the privacy protection model and framework design, using game theory for decision-making. Finally, by employing a comparison analysis, we perform some experiments to assess the effectiveness and superiority of the proposed game-theoretic model over the traditional solutions.

A Game-Theoretic Approach to Information-Flow Control via Protocol Composition

In the inference attacks studied in Quantitative Information Flow (QIF), the attacker typically tries to interfere with the system in the attempt to increase its leakage of secret information. The defender, on the other hand, typically tries to decrease leakage by introducing some controlled noise. This noise introduction can be modeled as a type of protocol composition, i.e., a probabilistic choice among different protocols, and its effect on the amount of leakage depends heavily on whether or not this choice is visible to the attacker. In this work, we consider operators for modeling visible and hidden choice in protocol composition, and we study their algebraic properties. We then formalize the interplay between defender and attacker in a game-theoretic framework adapted to the specific issues of QIF, where the payoff is information leakage. We consider various kinds of leakage games, depending on whether players act simultaneously or sequentially, and on whether or not the choices of the defender are visible to the attacker. In the case of sequential games, the choice of the second player is generally a function of the choice of the first player, and his/her probabilistic choice can be either over the possible functions (mixed strategy) or it can be on the result of the function (behavioral strategy). We show that when the attacker moves first in a sequential game with a hidden choice, then behavioral strategies are more advantageous for the defender than mixed strategies. This contrasts with the standard game theory, where the two types of strategies are equivalent. Finally, we establish a hierarchy of these games in terms of their information leakage and provide methods for finding optimal strategies (at the points of equilibrium) for both attacker and defender in the various cases.

A General Framework for the Assessment of Power System Vulnerability to Malicious Attacks

The protection and safe operations of power systems heavily rely on the identification of the causes of damage and service disruption. This article presents a general framework for the assessment of power system vulnerability to malicious attacks. The concept of susceptibility to an attack is employed to quantitatively evaluate the degree of exposure of the system and its components to intentional offensive actions. A scenario with two agents having opposing objectives is proposed, i.e., a defender having multiple alternatives of protection strategies for system elements, and an attacker having multiple alternatives of attack strategies against different combinations of system elements. The defender aims to minimize the system susceptibility to the attack, subject to budget constraints; on the other hand, the attacker aims to maximize the susceptibility. The problem is defined as a zero-sum game between the defender and the attacker. The assumption that the interests of the attacker and the defender are opposite makes it irrelevant whether or not the defender shows the strategy he/she will use. Thus, the approaches "leader-follower game" or "simultaneous game" do not provide differences as far as the results are concerned. The results show an example of such a situation, and the von Neumann theorem is applied to find the (mixed) equilibrium strategies of the attacker and of the defender.

Randomized Prediction Games for Adversarial Machine Learning

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.