Biblio

Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to govern nodes in the networks. The mission lies ahead in coordinating among such dynamically shifting nodes. The root problem of identifying and isolating misbehaving nodes that refuse to forward packets in multi-hop ad hoc networks is solved by the development of a comprehensive system called Audit-based Misbehavior Detection (AMD) that can efficiently isolates selective and continuous packet droppers. AMD evaluates node behavior on a per-packet basis, without using energy-expensive overhearing techniques or intensive acknowledgment schemes. Moreover, AMD can detect selective dropping attacks even in end-to-end encrypted traffic and can be applied to multi-channel networks. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. A non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a Audit-based Misbehaviour Detection (AMD)scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work GAMD significantly reduces the cost of detecting misbehavior nodes in the network.

We propose POWERALERT, an efficient external integrity checker for untrusted hosts. Current attestation systems suffer from shortcomings, including requiring a complete checksum of the code segment, from being static, use of timing information sourced from the untrusted machine, or using imprecise timing information such as network round-trip time. We address those shortcomings by (1) using power measurements from the host to ensure that the checking code is executed and (2) checking a subset of the kernel space over an extended period. We compare the power measurement against a learned power model of the execution of the machine and validate that the execution was not tampered. Finally, POWERALERT randomizes the integrity checking program to prevent the attacker from adapting. We model the interaction between POWERALERT and an attacker as a time-continuous game. The Nash equilibrium strategy of the game shows that POWERALERT has two optimal strategy choices: (1) aggressive checking that forces the attacker into hiding, or (2) slow checking that minimizes cost. We implement a prototype of POWERALERT using Raspberry Pi and evaluate the performance of the integrity checking program generation.

Reliable operation of power systems is a primary challenge for the system operators. With the advancement in technology and grid automation, power systems are becoming more vulnerable to cyber-attacks. The main goal of adversaries is to take advantage of these vulnerabilities and destabilize the system. This paper describes a game-theoretic approach to attacker / defender modeling in power systems. In our models, the attacker can strategically identify the subset of substations that maximize damage when compromised. However, the defender can identify the critical subset of substations to protect in order to minimize the damage when an attacker launches a cyber-attack. The algorithms for these models are applied to the standard IEEE-14, 39, and 57 bus examples to identify the critical set of substations given an attacker and a defender budget.

With the capability of support mobile computing demand with small delay, fog computing has gained tremendous popularity. Nevertheless, its highly virtualized environment is vulnerable to cyber attacks such as emerging Advanced Persistent Threats attack. In this paper, we propose a novel approach of cyber risk management for the fog computing platform. Particularly, we adopt the cyber-insurance as a tool for neutralizing cyber risks from fog computing platform. We consider a fog computing platform containing a group of fog nodes. The platform is composed of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform. Meanwhile, the attacker dynamically adjusts the allocation of its attack resources to improve the probability of successful attack. Additionally, to prevent from the potential loss due to attacks, the provider also makes a dynamic decision on the purchases ratio of cyber-insurance from the cyber-insurer for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. In our formulated dynamic Stackelberg game, the attacker and provider act as the followers, and the cyber-insurer acts as the leader. In the lower level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper level, the cyber-insurer optimizes its premium determination strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.

The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT- enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining the network connectivity in the presence of adversaries is a critical task for the infrastructure network operators. In this paper, we establish a three-player three-stage game-theoretic framework including two network operators and one attacker to capture the secure design of multi- layer infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of Internet of Battlefield Things (IoBT) to corroborate the obtained results.

In this paper, a novel game-theoretic framework is introduced to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, a dynamic, psychological network interdiction game is formulated between a soldier and an attacker. In this game, the soldier seeks to find the optimal path to minimize the time needed to reach a destination, while maintaining a desired bit error rate (BER) performance by selectively communicating with certain IoBT devices. The attacker, on the other hand, seeks to find the optimal IoBT devices to attack, so as to maximize the BER of the soldier and hinder the soldier's progress. In this game, the soldier and attacker's first- order and second-order beliefs on each others' behavior are formulated to capture their psychological behavior. Using tools from psychological game theory, the soldier and attacker's intention to harm one another is captured in their utilities, based on their beliefs. A psychological forward induction-based solution is proposed to solve the dynamic game. This approach can find a psychological sequential equilibrium of the game, upon convergence. Simulation results show that, whenever the soldier explicitly intends to frustrate the attacker, the soldier's material payoff is increased by up to 15.6% compared to a traditional dynamic Bayesian game.

In this paper, the problem of network connectivity is studied for an adversarial Internet of Battlefield Things (IoBT) system in which an attacker aims at disrupting the connectivity of the network by choosing to compromise one of the IoBT nodes at each time epoch. To counter such attacks, an IoBT defender attempts to reestablish the IoBT connectivity by either deploying new IoBT nodes or by changing the roles of existing nodes. This problem is formulated as a dynamic multistage Stackelberg connectivity game that extends classical connectivity games and that explicitly takes into account the characteristics and requirements of the IoBT network. In particular, the defender's payoff captures the IoBT latency as well as the sum of weights of disconnected nodes at each stage of the game. Due to the dependence of the attacker's and defender's actions at each stage of the game on the network state, the feedback Stackelberg solution [feedback Stackelberg equilibrium (FSE)] is used to solve the IoBT connectivity game. Then, sufficient conditions under which the IoBT system will remain connected, when the FSE solution is used, are determined analytically. Numerical results show that the expected number of disconnected sensors, when the FSE solution is used, decreases up to 46% compared to a baseline scenario in which a Stackelberg game with no feedback is used, and up to 43% compared to a baseline equal probability policy.

Cloud federations allow Cloud Service Providers (CSPs) to deliver more efficient service performance by interconnecting their Cloud environments and sharing their resources. However, the security of the federated Cloud service could be compromised if the resources are shared with relatively insecure and unreliable CSPs. In this paper, we propose a Cloud federation formation model that considers the security risk levels of CSPs. We start by quantifying the security risk of CSPs according to well defined evaluation criteria related to security risk avoidance and mitigation, then we model the Cloud federation formation process as a hedonic coalitional game with a preference relation that is based on the security risk levels and reputations of CSPs. We propose a federation formation algorithm that enables CSPs to cooperate while considering the security risk introduced to their infrastructures, and refrain from cooperating with undesirable CSPs. According to the stability-based solution concepts that we use to evaluate the game, the model shows that CSPs will be able to form acceptable federations on the fly to service incoming resource provisioning requests whenever required.

security evaluation of cryptosystem is a critical topic in cryptology. It is used to differentiate among cryptosystems' security. The aim of this paper is to produce a new model for security evaluation of cryptosystems, which is a combination of two theories (Game Theory and Information Theory). The result of evaluation method can help researchers to choose the appropriate cryptosystems in Wireless Communications Networks such as Cognitive Radio Networks.

Cyber security management of systems in the cyberspace has been a challenging problem for both practitioners and the research community. Their proprietary nature along with the complexity renders traditional approaches rather insufficient and creating the need for the adoption of a holistic point of view. This paper draws upon the principles theory game in order to present a novel systemic approach towards cyber security management, taking into account the complex inter-dependencies and providing cost-efficient defense solutions.

This article examines Usage of Game Theory in The Internet Wide Scan. There is compiled model of “Network Scanning” game. There is described process of players interaction in the coalition antagonistic and network games. The concept of target system's cost is suggested. Moreover, there is suggested its application in network scanning, particularly, when detecting honeypot/honeynet systems.

The paper presents the study of protecting wireless sensor network (WSNs) by using game theory for malicious node. By means of game theory the malicious attack nodes can be effectively modeled. In this research there is study on different game theoretic strategies for WSNs. Wireless sensor network are made upon the open shared medium which make easy to built attack. Jamming is the most serious security threats for information preservation. The key purpose of this paper is to present a general synopsis of jamming technique, a variety of types of jammers and its prevention technique by means of game theory. There is a network go through from numerous kind of external and internal attack. The jamming of attack that can be taking place because of the high communication inside the network execute by the nodes in the network. As soon as the weighty communications raise the power expenditure and network load also increases. In research work a game theoretic representation is define for the safe communication on the network.

We define a number of threat models to describe the goals, the available information and the actions characterising the behaviour of a possible attacker in multimedia forensic scenarios. We distinguish between an investigative scenario, wherein the forensic analysis is used to guide the investigative action and a use-in-court scenario, wherein forensic evidence must be defended during a lawsuit. We argue that the goals and actions of the attacker in these two cases are very different, thus exposing the forensic analyst to different challenges. Distinction is also made between model-based techniques and techniques based on machine learning, showing how in the latter case the necessity of defining a proper training set enriches the set of actions available to the attacker. By leveraging on the previous analysis, we then introduce some game-theoretic models to describe the interaction between the forensic analyst and the attacker in the investigative and use-in-court scenarios.

Intrusion Detection Systems (IDSs) are crucial security mechanisms widely deployed for critical network protection. However, conventional IDSs become incompetent due to the rapid growth in network size and the sophistication of large scale attacks. To mitigate this problem, Collaborative IDSs (CIDSs) have been proposed in literature. In CIDSs, a number of IDSs exchange their intrusion alerts and other relevant data so as to achieve better intrusion detection performance. Nevertheless, the required information exchange may result in privacy leakage, especially when these IDSs belong to different self-interested organizations. In order to obtain a quantitative understanding of the fundamental tradeoff between the intrusion detection accuracy and the organizations' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, we are able to derive the expected behaviors of both the attacker and the IDSs and obtain the utility-privacy tradeoff curve. In addition, the existence of Nash equilibrium (NE) is proved and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of IDSs. Finally, simulation results are shown to validate the analysis.

In this paper, we describe an abstraction-based method for synthesizing a state-based switching control for stabilizing a family of dynamical systems. Given a set of dynamical systems and a set of polyhedral switching surfaces, the algorithm synthesizes a strategy that assigns to every surface the linear dynamics to switch to at the surface. Our algorithm constructs a finite game graph that consists of the switching surfaces as the existential nodes and the choices of the dynamics as the universal nodes. In addition, the edges capture quantitative information about the evolution of the distance of the state from the equilibrium point along the executions. A switching strategy for the family of dynamical systems is extracted by finding a strategy on the game graph which results in plays having a bounded weight. Such a strategy is obtained by reducing the problem to the strategy synthesis for an energy game, which is a well-studied problem in the literature. We have implemented our algorithm for polyhedral inclusion dynamics and linear dynamics. We illustrate our algorithm on examples from these two classes of systems.

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.

In the cloud computing era, in order to avoid computational burdens, many organizations tend to outsource their computations to third-party cloud servers. In order to protect service quality, the integrity of computation results need to be guaranteed. In this paper, we develop a game theoretic framework which helps the outsourcer to maximize its payoff while ensuring the desired level of integrity for the outsourced computation. We define two Stackelberg games and analyze the optimal setting's sensitivity for the parameters of the model.

Ethereum contracts can be designed to function as fully decentralized applications called DAPPs that hold financial assets, and many have already been fielded. Unfortunately, DAPPs can be hacked, and the assets they control can be stolen. A recent attack on an Ethereum decentralized application called The DAO demonstrated that smart contract bugs are more than an academic concern. Ether worth hundreds of millions of US dollars was extracted by an attacker from The DAO, sending the value of its tokens and the overall exchange price of ether itself tumbling. We present two market-based techniques for insuring the ether holdings of a DAPP. These mechanisms exist and are managed as part of the core programming of the DAPP, rather than as separate mechanisms managed by users. Our first technique is based on futures contracts indexed by the trade price of ether for DAPP tokens. Under fairly general circumstances, our technique is capable of recovering the majority of ether lost from theft with high probability even when all of the ether holdings are stolen; and the only cost to DAPP token holders is an adjustable ether withdrawal fee. As a second, complementary, technique we propose the use of Gated Public Offerings (GPO) as a mechanism that mitigates the effects of attackers that exploit DAPP withdrawal vulnerabilities. We show that using more than one public offering round encourages attackers to exploit the vulnerability early, or depending on certain factors, to delay exploitation (possibly indefinitely) and short tokens in the market instead. In both cases, less ether is ultimately stolen from the DAPP, and in the later case, some of the losses are transferred to the market.

Many companies within the Internet of Things (IoT) sector rely on the personal data of users to deliver and monetize their services, creating a high demand for personal information. A user can be seen as making a series of transactions, each involving the exchange of personal data for a service. In this paper, we argue that privacy can be described quantitatively, using the game- theoretic concept of value of information (VoI), enabling us to assess whether each exchange is an advantageous one for the user. We introduce PrivacyGate, an extension to the Android operating system built for the purpose of studying privacy of IoT transactions. An example study, and its initial results, are provided to illustrate its capabilities.

Due to the increasing concerns of securing private information, context-aware Internet of Things (IoT) applications are in dire need of supporting data privacy preservation for users. In the past years, game theory has been widely applied to design secure and privacy-preserving protocols for users to counter various attacks, and most of the existing work is based on a two-player game model, i.e., a user/defender-attacker game. In this paper, we consider a more practical scenario which involves three players: a user, an attacker, and a service provider, and such a complicated system renders any two-player model inapplicable. To capture the complex interactions between the service provider, the user, and the attacker, we propose a hierarchical two-layer three-player game framework. Finally, we carry out a comprehensive numerical study to validate our proposed game framework and theoretical analysis.

Multiple transmit and receive antennas can be used to increase the number of independent streams between a transmitter-receiver pair, or to improve the interference resilience property with the help of linear minimum mean squared error (MMSE) receivers. An interference aware inter-cell rank coordination framework for the future fifth generation wireless system is proposed in this article. The proposal utilizes results from random matrix theory to estimate the mean signal-to-interference-plus-noise ratio at the MMSE receiver. In addition, a game-theoretic interference pricing measure is introduced as an inter-cell interference management mechanism to balance the spatial multiplexing vs. interference resilience trade-off. Exhaustive Monte Carlo simulations results demonstrating the performance of the proposed algorithm indicate a gain of around 40% over conventional non interference-aware schemes; and within around 6% of the optimum performance obtained using a brute-force exhaustive search algorithm.

Cloud computing has become an irreversible trend. Together comes the pressing need for verifiability, to assure the client the correctness of computation outsourced to the cloud. Existing verifiable computation techniques all have a high overhead, thus if being deployed in the clouds, would render cloud computing more expensive than the on-premises counterpart. To achieve verifiability at a reasonable cost, we leverage game theory and propose a smart contract based solution. In a nutshell, a client lets two clouds compute the same task, and uses smart contracts to stimulate tension, betrayal and distrust between the clouds, so that rational clouds will not collude and cheat. In the absence of collusion, verification of correctness can be done easily by crosschecking the results from the two clouds. We provide a formal analysis of the games induced by the contracts, and prove that the contracts will be effective under certain reasonable assumptions. By resorting to game theory and smart contracts, we are able to avoid heavy cryptographic protocols. The client only needs to pay two clouds to compute in the clear, and a small transaction fee to use the smart contracts. We also conducted a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network.

Blockchain has been applied to study data privacy and network security recently. In this paper, we propose a punishment scheme based on the action record on the blockchain to suppress the attack motivation of the edge servers and the mobile devices in the edge network. The interactions between a mobile device and an edge server are formulated as a blockchain security game, in which the mobile device sends a request to the server to obtain real-time service or launches attacks against the server for illegal security gains, and the server chooses to perform the request from the device or attack it. The Nash equilibria (NEs) of the game are derived and the conditions that each NE exists are provided to disclose how the punishment scheme impacts the adversary behaviors of the mobile device and the edge server.

With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.