Biblio

Can quantum entanglement increase the capacity of (classical) covert channels? To one familiar with Holevo's Theorem it is tempting to think that the answer is obviously no. However, in this work we show: quantum entanglement can in fact increase the capacity of a classical covert channel, in the presence of an active adversary; on the other hand, a zero-capacity channel is not improved by entanglement, so entanglement cannot create ‘purely quantum’ covert channels; the problem of determining the capacity of a given channel in the presence of entanglement is undecidable; but there is an algorithm to bound the entangled capacity of a channel from above, adapted from the semi-definite hierarchy from the theory of non-local games, whose close connection to channel capacity is at the core of all of our results.

This paper studies the problem of designing optimal privacy mechanism with less energy cost. The eavesdropper and the defender with limited resources should choose which channel to eavesdrop and defend, respectively. A zero-sum stochastic game framework is used to model the interaction between the two players and the game is solved through the Nash Q-learning approach. A numerical example is given to verify the proposed method.

In order to solve the problem that the traditional “centralized” access control technology can no longer guarantee the security of access control in the current Internet of Things (IoT)environment, a dynamic access control game mechanism based on trust is proposed. According to the reliability parameters of the recommended information obtained by the two elements of interaction time and the number of interactions, the user's trust value is dynamically calculated, and the user is activated and authorized to the role through the trust level corresponding to the trust value. The trust value and dynamic adjustment factor are introduced into the income function to carry out game analysis to avoid malicious access behavior of users. The hybrid Nash equilibrium strategy of both sides of the transaction realizes the access decision-making work in the IoT environment. Experimental results show that the game mechanism proposed in this paper has a certain restraining effect on malicious nodes and can play a certain incentive role in the legitimate access behavior of IoT users.

In this modern era, web security is often required to beware from fraudulent activities. There are several hackers try to build a program that can interact with web pages automatically and try to breach the data or make several junk entries due to that web servers get hanged. To stop the junk entries; CAPTCHA is a solution through which bots can be identified and denied the machine based program to intervene with. CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart. In the progression of CAPTCHA; there are several methods available such as distorted text, picture recognition, math solving and gaming based CAPTCHA. Game based turing test is very much popular now a day but there are several methods through which game can be cracked because game is not intellectual. So, there is a required of intrinsic CAPTCHA. The proposed system is based on Intrinsic Decision based Situation Reaction Challenge. The proposed system is able to better classify the humans and bots by its intrinsic problem. It has been considered as human is more capable to deal with the real life problems and machine is bit poor to understand the situation or how the problem can be solved. So, proposed system challenges with simple situations which is easier for human but almost impossible for bots. Human is required to use his common sense only and problem can be solved with few seconds.

The success of human-robot interaction is strongly affected by the people’s ability to infer others’ intentions and behaviours, and the level of people’s trust that others will abide by their same principles and social conventions to achieve a common goal. The ability of understanding and reasoning about other agents’ mental states is known as Theory of Mind (ToM). ToM and trust, therefore, are key factors in the positive outcome of human-robot interaction. We believe that a robot endowed with a ToM is able to gain people’s trust, even when this may occasionally make errors.In this work, we present a user study in the field in which participants (N=123) interacted with a robot that may or may not have a ToM, and may or may not exhibit erroneous behaviour. Our findings indicate that a robot with ToM is perceived as more reliable, and they trusted it more than a robot without a ToM even when the robot made errors. Finally, ToM results to be a key driver for tuning people’s trust in the robot even when the initial condition of the interaction changed (i.e., loss and regain of trust in a longer relationship).

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.

In this paper, we consider a discrete-time stochastic Stackelberg game where there is a defender (also called leader) who has to defend a target and an attacker (also called follower). The attacker has a private type that evolves as a controlled Markov process. The objective is to compute the stochastic Stackelberg equilibrium of the game where defender commits to a strategy. The attacker’s strategy is the best response to the defender strategy and defender’s strategy is optimum given the attacker plays the best response. In general, computing such equilibrium involves solving a fixed-point equation for the whole game. In this paper, we present an algorithm that computes such strategies by solving lower dimensional fixed-point equations for each time t. Based on this algorithm, we compute the Stackelberg equilibrium of a security example.

With the widespread application of power Internet of Things (IoT), the edge IoT agents are often threatened by various attacks, among which the white-box attack is the most serious. The white-box implementation of the cryptography algorithm can hide key information even in the white-box attack context by means of obfuscation. However, under the specially designed attack, there is still a risk of the information being recovered within a certain time complexity. In this paper, by introducing pseudo states, a new white-box implementation of SM4 algorithm is proposed. The encryption and decryption processes are implemented in the form of matrices and lookup tables, which are obfuscated by scrambling encodings. The introduction of pseudo states could complicate the obfuscation, leading to the great improvement in the security. The number of pseudo states can be changed according to the requirements of security. Through several quantitative indicators, including diversity, ambiguity, the time complexity required to extract the key and the value space of the key and external encodings, it is proved that the security of the proposed implementation could been enhanced significantly, compared with the existing schemes under similar memory occupation.

A new Domain Name System (DNS) cache poisoning attack aiming at clients has emerged recently. It induced cloud users to visit fake web sites and thus reveal information such as account passwords. However, the design of current DNS defense architecture does not formally consider the protection of clients. Although the DNS traffic encryption technology can alleviate this new attack, its deployment is as slow as the new DNS architecture. Thus we propose a lightweight adaptive intelligent defense strategy, which only needs to be deployed on the client without any configuration support of DNS. Firstly, we model the attack and defense process as a static stochastic game with incomplete information under bounded rationality conditions. Secondly, to solve the problem caused by uncertain attack strategies and large quantities of game states, we adopt a deep reinforcement learning (DRL) with guaranteed monotonic improvement. Finally, through the prototype system experiment in Alibaba Cloud, the effectiveness of our method is proved against multiple attack modes with a success rate of 97.5% approximately.

In this paper, we consider a sequential stochastic Stackelberg game with two players, a leader, and a follower. The follower observes the state of the system privately while the leader does not. Players play Stackelberg equilibrium where the follower plays best response to the leader's strategy. In such a scenario, the leader has the advantage of committing to a policy that maximizes its returns given the knowledge that the follower is going to play the best response to its policy. Such a pair of strategies of both the players is defined as Stackelberg equilibrium of the game. Recently, [1] provided a sequential decomposition algorithm to compute the Stackelberg equilibrium for such games which allow for the computation of Markovian equilibrium policies in linear time as opposed to double exponential, as before. In this paper, we extend that idea to the case when the state update dynamics are not known to the players, to propose an reinforcement learning (RL) algorithm based on Expected Sarsa that learns the Stackelberg equilibrium policy by simulating a model of the underlying Markov decision process (MDP). We use particle filters to estimate the belief update for a common agent that computes the optimal policy based on the information which is common to both the players. We present a security game example to illustrate the policy learned by our algorithm.

We propose a novel theoretical approach for solving a Stochastic Security Game using augmented Markov Decison Processes and an experimental evaluation. Most of the previous works mentioned in the literature focus on Linear Programming techniques seeking Strong Stackelberg Equilibria through the defender and attacker’s strategy spaces. Although effective, these techniques are computationally expensive and tend to not scale well to very large problems. By fixing the set of the possible defense strategies, our approach is able to use the well-known augmented MDP formalism to compute an optimal policy for an attacker facing a defender patrolling. Experimental results on fully observable cases validate our approach and show good performances in comparison with optimistic and pessimistic approaches. However, these results also highlight the need of scalability improvements and of handling the partial observability cases.

Cyber-Physical Systems(CPS) have been experiencing a fast-growing process in recent decades, and related security issues also have become more important than ever before. To design an efficient defensive policy for operators and controllers is the utmost task to be considered. In this paper, a stochastic game-theoretic model is developed to study a CPS security problem by considering the interdependence between cyber and physical spaces of a CPS. The game model is solved with Minimax Q-learning for finding the mixed strategies equilibria. The numerical simulation revealed that the defensive factors and attack cost can affect the policies adopted by the system. From the perspective of the operator of a CPS, increasing successful defense probability in the phrase of disruption will help to improve the probability of defense strategy when there is a correlation between the cyber layer and the physical layer in a CPS. On the contrary side, the system defense probability will decrease as the total cost of the physical layer increases.

Internet of Things (IoT) is becoming an emerging paradigm to provide pervasive connectivity where “anything“ can be connected “anywhere” at “anytime” via massive deployment of physical objects like sensors, controllers, and actuators. However, the open nature of wireless communications and the energy constraint of the IoT devices impose strong security concerns. In this context, traditional cryptographic techniques may not be suitable in such a resource-constrained network. To address this problem, an effective security solution that ensures a trade-off between security effectiveness and energy efficiency is required. In this paper, we exploit cooperative transmission between sensor nodes in IoT for e-Health application, as a promising technique to enhance the physical layer security of wireless communications in terms of secrecy capacity while considering the resource-impoverished devices. Specifically, we propose a dynamic and cooperative virtual multiple-input and multiple-output (MIMO) configuration approach based on game theory to preserve the confidentiality of the transmitted messages with high energy savings. For this purpose, we model the physical layer security cooperation problem as a non-transferable coalition formation game. The set of cooperative devices form a virtual dynamically-configured MIMO network that is able to securely and efficiently transmit data to the destination. Simulation results show that the proposed game-based virtual MIMO configuration approach can improve the average secrecy capacity per device as well as the network lifetime compared to non-cooperative transmission.

The network attack such as Distributed Denial-of-Service (DDoS) attack could be critical to latency-critical systems such as Mobile Edge Computing (MEC) as such attacks significantly increase the response delay of the victim service. Intrusion prevention system (IPS) is a promising solution to defend against such attacks, but there will be a trade-off between IPS deployment and application resource reservation as the deployment of IPS will reduce the number of computation resources for MEC applications. In this paper, we proposed a game-theoretic framework to study the joint computation resource allocation and IPS deployment in the MEC architecture. We study the pricing strategy of the MEC platform operator and purchase strategy of the application service provider, given the expected attack strength and end user demands. The best responses of both MPO and ASPs are derived theoretically to identify the Stackelberg equilibrium. The simulation results confirm that the proposed solutions significantly increase the social welfare of the system.

In the Bitcoin mining network, miners contribute computation power to solve crypto-puzzles in exchange for financial rewards. Due to the randomness and the competitiveness of mining, individual miners tend to join mining pools for low risks and steady incomes. Usually, a pool is managed by its central operator, who charges fees for providing risk-sharing services. This paper presents a hierarchical distributed computation paradigm where miners can distribute their power among multiple pools. By adding virtual pools, we separate miners’ dual roles of being the operator as well as being the member when solo mining. We formulate a multi-leader multi-follower Stackelberg game to study the joint utility maximization of pool operators and miners, thereby addressing a computation power allocation problem. We investigate two practical pool operation modes, a uniform-share-difficulty mode and a nonuniform-share-difficulty mode. We derive analytical results for the Stackelberg equilibrium of the game under both modes, based on which optimal strategies are designed for all operators and miners. Numerical evaluations are presented to verify the proposed model.

We study a game-theoretic model of the interactions between a Cyber-Physical System’s (CPS) operator (the defender) against an attacker who launches stepping-stone attacks to reach critical assets within the CPS. We consider that, in addition to optimally allocating its security budget to protect the assets, the defender may choose to modify the CPS through network design interventions. In particular, we propose and motivate four ways in which the defender can introduce additional nodes in the CPS: these nodes may be intended as additional safeguards, be added for functional or structural redundancies, or introduce additional functionalities in the system. We analyze the security implications of each of these design interventions, and evaluate their impacts on the security of an automotive network as our case study. We motivate the choice of the attack graph for this case study and elaborate how the parameters in the resulting security game are selected using the CVSS metrics and the ISO-26262 ASIL ratings as guidance. We then use numerical experiments to verify and evaluate how our proposed network interventions may be used to guide improvements in automotive security.

Considered is a network of parallel wireless channels in which individual parties are engaged in secret communication under the protection of cooperative jamming. A strategic eavesdropper selects the most vulnerable channels to attack. Existing works usually suggest the defender allocate limited cooperative jamming power to various channels. However, it usually requires some strong assumptions and complex computation to find such an optimal power control policy. This paper proposes a probabilistic cooperative jamming scheme such that the defender focuses on protecting randomly selected channels. Two different cases regarding each channel’s eavesdropping capacity are discussed. The first case studies the general scenario where each channel has different eavesdropping capacity. The second case analyzes an extreme scenario where all channels have the same eavesdropping capacity. Two non-zero-sum Nash games model the competition between the network defender and an eavesdropper in each case. Furthermore, considering the case that the defender does not know the eavesdropper’s channel state information (CSI) leads to a Bayesian game. For all three games, we derive conditions for the existence of a unique Nash equilibrium (NE), and obtain the equilibria and the value functions in closed form.

5G and beyond 5G low power wireless networks make Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications capable of serving massive amounts of devices and machines. Due to the broadcast nature of wireless networks, it is crucial to secure the communication between these devices and machines from spoofing and interception attacks. This paper is concerned with the security of carrier frequency offset (CFO) based continuous physical layer authentication. The interaction between an attacker and a defender is modeled as a dynamic discrete leader-follower game with imperfect information. In the considered model, a legitimate user (Alice) communicates with the defender/operator (Bob) and is authorized by her CFO continuously. The attacker (Eve), by listening/eavesdropping the communication between Alice and Bob, tries to learn the CFO characteristics of Alice and aims to inject malicious packets to Bob by impersonating Alice. First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. Extensive simulations illustrate the characteristics of optimal strategies/utilities of the players depending on the actions, and show that the defender’s optimal false positive rate causes attack success probabilities to be in the order of 0.99. The results show the importance of the parameters while finding the balance between system security and efficiency.

The user's access history can be used as an important reference factor in determining whether to allow the current access request or not. And it is often ignored by the existing access control models. To make up for this defect, a Dynamic Trust - game theoretic Access Control model is proposed based on the previous work. This paper proposes a method to quantify the user's trust in the cloud environment, which uses identity trust, behavior trust, and reputation trust as metrics. By modeling the access process as a game and introducing the user's trust value into the pay-off matrix, the mixed strategy Nash equilibrium of cloud user and service provider is calculated respectively. Further, a calculation method for the threshold predefined by the service provider is proposed. Authorization of the access request depends on the comparison of the calculated probability of the user's adopting a malicious access policy with the threshold. Finally, we summarize this paper and make a prospect for future work.

Game theory is a branch of modern mathematics, which is a mathematical method to study how decision-makers should make decisions in order to strive for the maximum interests in the process of competition. In this paper, from the perspective of offensive and defensive confrontation, using game theory for reference, we build a dynamic evaluation model of information system security risk based on static game model. By using heisani transformation, the uncertainty of strategic risk of offensive and defensive sides is transformed into the uncertainty of each other's type. The security risk of pure defense strategy and mixed defense strategy is analyzed quantitatively, On this basis, an information security risk assessment algorithm based on static game model is designed.

The growing use of digital media has been accompanied by an increase of the risks associated with the use of information systems, notably cybersecurity risks. In turn, the increasing use of information systems has an impact on users' media and information literacy. This research aims to address the relationship between media and information literacy, and the adoption of risky cybersecurity behaviours. This approach will be carried out through the definition of a conceptual framework supported by a literature review, and a quantitative research of the relationships mentioned earlier considering a sample composed by students of a Higher Education Institution.

The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behaviour of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation.

Blockchain is becoming more popular because of its decentralized, secured, and transparent nature. Supply chain and its management is indispensable to improve customer services, reduce operating costs and improve financial position of a firm. Integration of blockchain and supply chain is substantial, but it alone is not enough for the sustainability of supply chain systems. The proposed mechanism speaks about the method of rewarding the supply chain parties with incentives so as to improve the security and make the integration of supply chain with blockchain sustainable. The proposed incentive mechanism employs the co-operative approach of game theory where all the supply chain parties show a cooperative behavior of following the blockchain-based supply chain protocols and also this mechanism makes a fair attempt in rewarding the supply chain parties with incentives.

The federated learning scheme enhances the privacy preservation through avoiding the private data uploading in cloud-edge computing. However, the attacks against the uploaded model updates still cause private data leakage which demotivates the privacy-sensitive participating edge devices. Facing this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the edge devices are motivated to actively contribute to the updated model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We formulate the incentive design problem as a three-layer Stackelberg game, where the server-device interaction is further formulated as a contract design problem. Extensive numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.

With the continuous development of edge computing, the application scope of mobile crowdsourcing (MCS) is constantly increasing. The distributed nature of edge computing can transmit data at the edge of processing to meet the needs of low latency. The trustworthiness of the third-party platform will affect the level of privacy protection, because managers of the platform may disclose the information of workers. Anonymous servers also belong to third-party platforms. For unreal third-party platforms, this paper recommends that workers first use the localized differential privacy mechanism to interfere with the real location information, and then upload it to an anonymous server to request services, called the localized differential anonymous privacy protection mechanism (LDNP). The two privacy protection mechanisms further enhance privacy protection, but exacerbate the loss of service quality. Therefore, this paper proposes to give corresponding compensation based on the authenticity of the location information uploaded by workers, so as to encourage more workers to upload real location information. Through comparative experiments on real data, the LDNP algorithm not only protects the location privacy of workers, but also maintains the availability of data. The simulation experiment verifies the effectiveness of the incentive mechanism.