Stackelberg Games for Blockchain Malware Detection: A Multi-Dimensional Viability Framework

In this paper, we consider the energy management problem for smart cyber-physical power systems with conventional utility companies, microgrids (MGs) and customers. We have adopted a game-theoretical approach to model the problem as a hierarchical game, which takes the interactions and interconnections among utility companies, MGs and customers into consideration. We have considered two completely different situations depending on whether utility companies can enforce their strategies upon MGs and customers, and have modelled the problem as a two-stage Stackelberg game and three-stage Stackelberg game, respectively. The backward induction method is used to analyse the proposed games and closed-form expressions are derived for optimum strategies. We prove that there exists a unique Nash equilibrium for each non-cooperative price competition game, and these Nash equilibria constitute the Stackelberg equilibrium. Simulation results show the effectiveness of the proposed algorithm and the relationships among system parameters.

Mobile Internet of Things (IoT) users possess dynamic service requirements in a real IoT system and always try to get services from trusted entities, which we term as facility nodes. However, the trustworthiness of the facility nodes varies from location to location, depending upon the service quality and user feedback. Therefore, a service provider may have different trust scores for its deployed facility nodes in different locations. It is always crucial for an IoT user to ensure that service has been taken from trustworthy nodes. In this article, we consider an IoT service provisioning architecture, where an IoT user requests services from nearby trusted nodes that are deployed by service providers other than the user's own registered service provider. To serve the requests from these unregistered users, the facility nodes incur additional costs. Furthermore, due to heterogeneous service requirements, IoT users prefer composite microservices from a set of facility nodes. In such cases, it is difficult to design a pricing scheme to incentivize the facility nodes. Therefore, we propose a pricing mechanism based on a two-stage Stackelberg game and analyze the interactions between the IoT users and the facility nodes, while considering the trust scores and service charges. In the game model, the facility nodes act as the leaders, and the IoT users act as the followers. Using backward induction, we analyze the existence and uniqueness of the Stackelberg equilibrium. Finally, the numerical results based on the mobility traces of a real map show that the proposed scheme incentivizes both the IoT users and the facility nodes, while ensuring trusted services.

With the rapid development of the Internet of Vehicles (IoV), vehicle networks will generate large amounts of data. Vehicle-to-Vehicle (V2V) data sharing can improve driving experience and quality of service, and Vehicle-to-Roadside unit (V2R) communication can improve road transport efficiency. A certain amount of bandwidth is consumed in the process of data sharing between V2V and V2R, and when multiple vehicles request data from the RSU at the same time, there may be network delay or congestion. Therefore, the vehicle obtains data faster by requesting high bandwidth from the RSU. However, since the bandwidth resources of RSUs are limited, optimal bandwidth service strategies need to be investigated. In addition, in order to ensure the safety, efficiency and traceability of data sharing between V2V and V2R, a blockchain-supported IoV needs to be established. Therefore, we design a blockchain-based framework for V2V and V2R transaction framework that integrates DAG blockchain and modern cryptography. The framework ensures security and scalability during the execution of data exchange and completion of bandwidth transactions, improves the service quality of vehicles and realizes decentralized payment. In this work, a two-stage Stackelberg game is established under two pricing schemes of uniform pricing and discriminatory pricing to jointly maximize the benefits of vehicles and RSUs. In Stage I, the RSU sets the corresponding bandwidth service and pricing strategy. In Stage II, the vehicle decides the service demand according to the observed pricing strategy. The optimal bandwidth allocation and pricing strategy between the RSU and the vehicle is determined through the game of both parties, and the existence and uniqueness of the two-stage subgame Nash equilibrium is analyzed by applying backward induction. The correctness of the model is verified by experiments, and the comparative analysis shows that the proposed model has high throughput.

In this paper, we focus on the privacy preserving mechanism design for crowdsourced Federated Learning (FL), where a requester can outsource its model training task to some workers via an FL platform. A potential way to preserve the privacy of workers' local data is to leverage Differential Privacy (DP) mechanisms on local models. However, most of these studies cannot allow workers to dominate their own privacy protection levels by themselves. Thus, we propose a Personalized Privacy Preserving Mechanism, called P3M, to satisfy the heterogeneous privacy needs of workers, which consists of two parts. The first part includes a personalized privacy budget determination problem. We model it as a two-stage Stackelberg game, derive the personalized privacy budget for each worker and the optimal payment for the requester, and prove that they form a unique Stackelberg equilibrium. Second, we design a dynamic perturbation scheme to perturb model parameters. Through the theoretical analysis, we prove that P3M satisfies the desired DP property, and derive the bounds of the variance of average perturbed parameters and the convergence upper bound. This demonstrates that the global model accuracy can be controllable and P3M is endowed with the satisfactory convergence performance. In addition, we extend our problem to the scenario where the total privacy budget of all workers is limited, so as to prevent some workers from setting exorbitant privacy budgets. Under the privacy constraint, we re-determine the personalized privacy budget for each worker. Finally, exhaustive simulations of P3M are conducted based on real-world datasets, and the experimental results corroborate its effectiveness and practicability.

Due to the rapid advancement of mobile communication technology, mobile devices nowadays can support a variety of data services that are not traditionally available. With the growing popularity of mobile devices in the last few years, attacks targeting them are also surging. Existing mobile malware detection techniques, which are often borrowed from solutions to Internet malware detection, do not perform as effectively due to the limited computing resources on mobile devices. In this paper, we propose VirusMeter, a novel and general malware detection method, to detect anomalous behaviors on mobile devices. The rationale underlying VirusMeter is the fact that mobile devices are usually battery powered and any malicious activity would inevitably consume some battery power. By monitoring power consumption on a mobile device, VirusMeter catches misbehaviors that lead to abnormal power consumption. For this purpose, VirusMeter relies on a concise user-centric power model that characterizes power consumption of common user behaviors. In a real-time mode, VirusMeter can perform fast malware detection with trivial runtime overhead. When the battery is charging (referred to as a battery-charging mode), VirusMeter applies more sophisticated machine learning techniques to further improve the detection accuracy. To demonstrate its feasibility and effectiveness, we have implemented a VirusMeter prototype on Nokia 5500 Sport and used it to evaluate some real cellphone malware, including FlexiSPY and Cabir. Our experimental results show that VirusMeter can effectively detect these malware activities with less than 1.5% additional power consumption in real time.

In a two-stage Stackelberg game, depending on the leader’s information about the choice of the follower among his optimal responses, one can associate different types of mathematical problems. We present formulations and solution concepts for such problems, together with their possible roles in bilevel optimization, and we illustrate the crucial issues concerning these solution concepts. Then, we discuss which of these issues can be positively or negatively answered and how managing the latter ones by means of two widely used approaches: regularizing the set of optimal responses of the follower, via different types of approximate solutions, or regularizing the follower’s payoff function, via the Tikhonov or the proximal regularizations. The first approach allows to define different kinds of regularized problems whose solutions exist and are stable under perturbations assuming sufficiently general conditions. Moreover, when the original problem has no solutions, we consider suitable regularizations of the second-stage problem, called inner regularizations, which enable to construct a surrogate solution, called viscosity solution, to the original problem. The second approach permits to overcome the non-uniqueness of the follower’s optimal response, by constructing sequences of Stackelberg games with a unique second-stage solution which approximate in some sense the original game, and to select among the solutions by using appropriate constructive methods.

The introduction of micro grids (MGs) into the utility grid poses new challenges in the energy management design due to the intermittent characteristics of renewable energy sources and limited storage capacity. In this paper, we proposed a distributed energy management algorithm by taking into consideration the interactions and interconnections among utility companies, MGs, and customers. We model the energy management problem as a two-stage Stackel berg game, in which utility companies and MGs are game leaders, and customers are game followers. Utility companies and MGs make decisions about what price to offer their electricity to customers. Customers adjust their electricity procurement amounts based on the prices offered by utility companies and MGs. We prove that a Nash equilibrium exists in the proposed two-stage Stackel berg game, and the optimum solutions obtained by the distributed energy management algorithm is exactly the Nash equilibrium. We have analyzed and verified the relationships among utility functions, electricity prices, electricity demands, electricity procurement amounts, and pollutant parameters through computer simulations. We have also compared the performance of the proposed distributed algorithm with the centralized algorithm under different simulation conditions.

We formulate a growth model with two heterogeneous agents in which there are knowledge spillovers from one agent to the other. This externality is assumed to be exogenous for the agents' decision problem. Then we compare its equilibrium paths with that in a game theoretic framework in which this externality gives rise to strategic interactions. By allowing for strategic interactions, our model becomes a two person dynamic game for which we show existence of an interior Markov Perfect Equilibrium (MPE) as well as the existence of an open-loop equilibrium. We characterize the qualitative properties of these equilibria and study their equilibrium dynamics and compare those to the solution of the exogenous growth model with externality. We find that the dynamics of the different models are similar but growth rates will be higher in the game than in the exogenous growth problem.

Traditional mobile crowdsensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users with smart devices. As crowdsensing service provider involves data collection from mobile users, the issue of providing rewards to incentivize mobile users is fundamentally important to ensure the sustainability of crowdsensing system. Recently, socially-aware crowdsensing services have been introduced as the integration of social networks and crowdsensing platforms. For example, in health-related crowdsensing applications, a mobile user benefits from information regarding food, exercise, medicine and medical treatment collected and shared by her socially-connected friends and family members. In this thesis, incentive mechanism design is revisited in the context of socially-aware crowdsensing, where the game theory tool is used to study the interactions between crowdsensing service provider and mobile users. In the first part of the thesis, incentive mechanism is designed by considering the underlying social network effects amid mobile social networks, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbors in social networks. The process of rewarding and participating is modeled as a two-stage game, and backward induction is used to analyze the mobile users' participation level and the crowdsensing service provider's optimal reward mechanism. The analytical expressions are derived for the discriminatory incentive as well as the uniform incentive mechanisms. In the second part of the thesis, information asymmetry challenges are investigated. A Bayesian Stackelberg game with incomplete information is formulated to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, among mobile users is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. To be more general and practical, different with the former two parts, incentive mechanism in presence of multiple crowdsensing service providers is studied in the third part of the thesis. Understanding the behaviors of mobile users and service providers in socially-aware crowdsensing is of paramount importance for incentive mechanisms. With this focus, a multi-leader and multi-follower Stackelberg game approach is proposed to model the strategic interactions among service providers and mobile users, where the social influence of mobile users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, the existence and uniqueness of the Stackelberg equilibrium are theoretically proved. In summary, this thesis addresses a few urgent challenging problems with the tools of game theory in the real implementation of incentive mechanism design in the context of socially-aware crowdsensing. Extensive numerical simulations are also conducted to illustrate some important properties of the equilibrium. Finally, several promising research directions are outlined for the future work.

As data exchange over wired and wireless networks continues to increase, the damage caused by malicious activities hidden in the data is also rising. In particular, malicious actions embedded in document files (e.g., PDF, HWP) are not only difficult to detect, but users are often careless when opening such files, making them highly vulnerable to malicious actions in documents. This study proposes a novel deep learning model that directly analyzes byte sequences to detect malicious actions embedded in HWP documents. Most previously proposed detection models have relied on convolutional neural networks, whereas our model uses no convolutional layers and employs two pooling layers instead. For the experiments, we constructed a new dataset by sampling byte sequences from HWP files, and our model achieved a 63.54% macro F1 score that is better than other existing models. This result demonstrates that our model is not only efficient but also achieves higher malware detection performance, implying that our model is more practical for real-world malware detection services, as we encounter numerous document files in everyday use.

Malware is a serious threat that weakens computer security. Malware detection is a branch of computer security concerned with the investigation and prevention of malicious software. It is not the only way to protect a company from a cyber-attack. Companies must analyse their risks and vulnerabilities in order to be effective. With this increase in cyber fraud and other malicious activities, traditional methods are not enough to block computers from it as this method has many drawbacks. In order to tackle these issues, researchers have been developing new techniques such as heuristic analysis, and static & dynamic analysis which can detect more than 90% of malware samples. In this paper, we will look at various techniques for detecting computer malware and malicious websites, as well as a versatile framework in which different machine-learning algorithms can be used to successfully distinguish between malware and clean files while attempting to reduce the number of false positives. In this paper, we have reviewed the rise of computer malware and how traditional methods of detection are being replaced by innovative techniques such as the behavioural-based model and the signature-based model.

CTIMD: Cyber threat intelligence enhanced malware detection using API call sequences with parameters

The present monograph focuses on the detection problem in adversarial setting. When framed in an adversarial setting, classical detection theory can not be applied any more, since, in order to make a correct decision, the presence of an adversary must be taken into account when designing the detector. In particular, the interplay between the Defender (D), wishing to carry out the detection task, and the Attacker (A ), aiming at impeding it, must be investigated. The purpose of this monograph is to lay out the foundations of a general theory of adversarial detection, taking into account the impact that the presence of the adversary has on the design of the optimal detector. We do so by casting the adversarial detection problem into a game theoretical framework, which is then studied by relying on typical methods of information theory. As a final result, the theory allows to state the conditions under which both the false positive and false negative error probabilities tend to zero exponentially fast, and to relate the error exponents of the two kinds of errors to the distortion the attacker can introduce into the test sequence.

In this paper, we consider the problem of traffic offloading in a two-tier downlink smallcell network with hybrid access. We formulate a Stackelberg game with pre-offloading decision, which includes two steps, to study the problem. The utility of the MBS is designed as a piecewise function, corresponding to the two possible decisions of the MBS. We design a bonus-based incentive mechanism to motivate smallcell base stations (SBSs) serve the MUEs. We prove the existence and uniqueness of the Stackelberg equilibrium. We also consider the incomplete information game, where the SBSs have private information. A Bayesian Stackelberg game with pre-offloading decision is formulated and the existence of Bayesian Stackelberg equilibrium is proved. Our proposed approach achieves much higher utilities than the existing approach does under some circumstances in both cases, which proves the necessity and superiority of our proposed game structure.

The shape of cost functions significantly influences the solution of optimization problems related to the treatment of infectious diseases. The classical linear or quadratic cost functions are widely used in the literature more to make the problems tractable than for their relevance with the model studied. For example, they do not seem appropriate when expensive therapies are required to treat a large number of patients, as in the case of the treatment of hepatitis C by sofosbuvir. In this paper, we study the strategic interactions between pharmaceutical companies and public authorities to determine the price of patented drugs and the number of treated infected people. A Stackelberg game and a Nash bargaining game are studied. In particular, we analyze the relationship between the different cost functions and the equilibria of the games.