基于移动目标防御信号博弈的容器迁移策略

容器作为虚拟机的轻量级替代产品,以其灵活、高效的特点促进了云计算的发展,但同时也面临着同驻攻击、逃逸攻击等安全威胁。针对云环境中的容器安全威胁,构建了基于移动目标防御的信号博弈模型,并提出了多阶段最优防御策略求解算法,通过博弈模型和求解算法选取最优策略,同时通过容器调度方法对容器进行调度,可以增强容器安全性。仿真实验结果表明,提出的迁移策略获取的防御收益相较于Kubernetes自带迁移策略提升了3.6倍,同时容器同驻率降低了79.62%,对现实容器云环境下的防御策略选取和安全性增强具有一定的借鉴意义。     

基于多阶段演化信号博弈模型的移动目标防御决策算法

当前网络安全事故频发,传统被动防御技术已经无法应对未知的网络安全威胁。针对这一问题,构建了多阶段演化信号博弈模型,并以防御方主动发射诱导信号进行安全防御为背景,提出了一种基于多阶段演化信号博弈模型的移动目标防御（MTD）决策算法。首先,以博弈双方不完全信息约束及完全理性前提为假设对模型的基本元素进行定义并进行模型整体理论分析;然后,设计了攻防策略的收益量化方法,并给出了详细的最优策略均衡求解过程;最后,引入MTD方法分析多阶段攻防情况下双方策略的演化趋势。实验结果表明,所提算法能准确预测出不同阶段最优防御策略,而且对新型网络主动防御技术研究具有指导意义。同时,通过蒙特卡洛仿真实验,将所提算法与传统随机均匀策略选择算法进行对比,所得结果验证了所提算法的有效性和安全性。     

面向SDN的移动目标防御技术研究进展

软件定义网络是基于开放标准的灵活架构,通过控制层管理网络功能和服务,具有控转分离、集中控制的特性;移动目标防御技术致力于构建一个不断变换的环境以提高网络系统的视在不确定性,需要灵活可定制、集中可控制的网络架构加以实施,因此将移动目标防御与软件定义网络相结合已成为更具应用价值研究热点。首先,分别介绍了软件定义网络和移动目标防御的基本概念,概括了软件定义网络所面临的安全威胁,阐述了面向SDN的移动目标防御的实现模型;其次,分别从SDN数据层、控制层和应用层归纳了移动目标防御的技术方法;最后,总结了现有SDN动态防御面临的挑战,对面向SDN的移动目标防御技术发展方向进行了展望。     

基于演化博弈的拟态防御策略优化

网络空间拟态防御是近些年出现的一种主动防御理论,以异构冗余和动态反馈机制不断调整执行环境来抵抗攻击。然而,面对黑客的多样化攻击手段,仅凭借拟态防御抵抗攻击是不安全的。为了增强系统的安全防御能力,本文在目前已有的防御系统基础上提出更为合理的防御选取方法。将有限理性的演化博弈引入到拟态防御中,构建了由攻击者、防御者和合法用户组成的三方演化博弈模型,并提出了最优防御策略求解方法。该博弈模型利用复制动态方程得到了演化稳定策略。仿真实验结果表明,系统通过执行推理的演化稳定策略可以降低损失,遏制攻击方的攻击行为,对拟态防御系统中防御策略选取和安全性增强具有一定的借鉴意义。     

敌对攻击环境下基于移动目标防御的算法稳健性增强方法

传统的机器学习模型工作在良性环境中,通常假设训练数据和测试数据是同分布的,但在恶意文档检测等领域该假设被打破。敌人通过修改测试样本对分类算法展开攻击,使精巧构造的恶意样本能够逃过机器学习算法的检测。为了提高机器学习算法的安全性,提出了基于移动目标防御技术的算法稳健性增强方法。实验证明,该方法通过在算法模型、特征选择、结果输出等阶段的动态变换,能够有效抵御攻击者对检测算法的逃逸攻击。     

Multi-agent team cooperation: A game theory approach

The main goal of this work is to design a team of agents that can accomplish consensus over a common value for the agents’ output using cooperative game theory approach. A semi-decentralized optimal control strategy that was recently introduced by the authors is utilized that is based on minimization of individual cost using local information. Cooperative game theory is then used to ensure team cooperation by considering a combination of individual cost as a team cost function. Minimization of this cost function results in a set of Pareto-efficient solutions. Among the Pareto-efficient solutions the Nash-bargaining solution is chosen. The Nash-bargaining solution is obtained by maximizing the product of the difference between the costs achieved through the optimal control strategy and the one obtained through the Pareto-efficient solution. The latter solution results in a lower cost for each agent at the expense of requiring full information set. To avoid this drawback some constraints are added to the structure of the controller that is suggested for the entire team using the linear matrix inequality (LMI) formulation of the minimization problem. Consequently, although the controller is designed to minimize a unique team cost function, it only uses the available information set for each agent. A comparison between the average cost that is obtained by using the above two methods is conducted to illustrate the performance capabilities of our proposed solutions.     

A game theory framework for ISP streaming traffic management

The overlay/underlay topology mismatch affects the performance of existing P2P platforms that can generate large volumes of unnecessary inter-ISP network traffic. Although recent works have shown the benefits of network awareness P2P solutions, no studies have focused on the investigation of the ISP behavior and their cooperative/non-cooperative attitudes.This paper proposes a game theoretic framework to help the design of techniques promoting the ISP cooperation in P2P streaming platforms and decreasing unnecessary inter-domain streaming traffic.We first analyze some simple scenarios to discuss the existence of Nash equilibria, the Pareto optimality, and a fairness criterion to refine the equilibrium points. Moreover, we apply ideas from Evolutionary Game Theory to design a distributed schemata that the ISPs can use to reach “socially acceptable” equilibrium points in a large ISP population. Furthermore, we develop a discrete event simulation to evaluate the effectiveness of the Evolutionary Game Theory framework.The study presented in the paper shows that the proposed strategies can effectively stimulate ISP cooperation aiming at the minimization of inter-ISP traffic and help to provide reliable P2P streaming service.     

视觉跟踪技术研究现状及其展望*

对运动目标进行视觉跟踪是机器人技术、计算机视觉等领域研究的热点之一。首先就跟踪技术的研究历史和现状进行回顾和分析,论述按照基于目标区域的跟踪、基于目标特征的跟踪、基于变形模板的跟踪和基于目标模型的跟踪四个分支分别展开;其次,对人们在该领域研究所面临的难点进行了归纳和总结;最后对分形理论和生物视觉在跟踪策略中的应用前景进行了展望。     

New trends on moving object detection in video images captured by a moving camera: A survey

This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories; modeling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.     

Capacity sharing in a network of independent factories: A cooperative game theory approach

Market changes, uncertainties and unpredictability characterize the market environment nowadays. The possibility to cooperate in a network allows companies to stay competitive. This paper proposes a game theory coordination mechanism for a network of independent plants; the approach is based on the OWEN’s theorem properly simulated in order to investigate different performances in various environmental conditions. To support the network and face the capacity sharing issue, a Multi-Agent Architecture has been specifically developed. The simulation results show that the proposed mechanism could be considered a valid alternative to the more usual negotiation approaches in several market conditions. The plants gain considerable benefits by participating in a network supported by the cooperative mechanism proposed.     

Selective Smooth Fictitious Play: An approach based on game theory for patrolling infrastructures with a multi-robot system

The multi-robot patrolling problem is defined as the activity of traversing a given environment. In this activity, a fleet of robots visits some places at irregular intervals of time for security purpose. To date, this problem has been solved with different approaches. However, the approaches that obtain the best results are unfeasible for security applications because they are centralized and deterministic. To overcome the disadvantages of previous work, this paper presents a new distributed and non-deterministic approach based on a model from game theory called Smooth Fictitious Play. To this end, the multi-robot patrolling problem is formulated by using concepts of graph theory to represent an environment. In this formulation, several normal-form games are defined at each node of the graph. This approach is validated by comparison with best suited literature approaches by using a patrolling simulator. The results for the proposed approach turn out to be better than previous literature approaches in as many as 88% of the cases of study. Moreover, the novel approach presented in this work has many advantages over other approaches of the literature such distribution, robustness, scalability, and dynamism. The achievements obtained in this work validate the potential of game theory to protect infrastructures.     

Channel assignment in heterogeneous multi-radio multi-channel wireless networks: A game theoretic approach

Channel assignment is a challenging issue for multi-radio multi-channel wireless networks, especially in a competing environment. This paper investigates channel assignment for selfish nodes in a heterogeneous scenario, in which nodes may have different QoS requirements and thus compete for different channels with unequal bandwidth. The interaction among nodes is formulated as a non-cooperative Multi-radio Channel Assignment Game (MCAG), where Nash Equilibrium (NE) corresponds to a stable channel assignment outcome from which no individual node has the incentive to deviate. The NEs in MCAG are characterized in this paper. Since multiple NEs may exist in this game, it is natural to choose the NE that maximizes the network utility, i.e., the sum of node utilities. It is shown that the optimal NE outcome can be derived by solving an integer non-linear programming problem. Based on some observations on the radio number distribution of NE, we propose a two-stage optimization algorithm to achieve an optimal channel assignment. Finally, computer simulations validate the effectiveness of the proposed algorithm.     

A low complexity algorithm for across range unit effect correction of the moving target via range frequency polynomial-phase transform

Based on the moving target detection, this paper proposes a low complexity algorithm for moving target across range unit effect correction on the basis of the range frequency polynomial-phase transform, utilizing which to remove the coupling of the range frequency and the slow time, then the across range unit effect can be corrected effectively. The algorithm does not need to search the target's parameters and estimate the Doppler ambiguity integer, and its computational complexity is low. The simulation results have proved the effectiveness of the proposed algorithm.     

When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity

Individuals within organizations are beginning to make an important realization: more information technology (IT) usage in the workplace can, at times, lead to productivity losses. We conceptualize this frequently observed, but largely ignored phenomenon as technology overload, when additional technology tools begin to crowd out one’s productivity instead of enhancing it. We found support for three main factors contributing technology-based productivity losses through information overload, communication overload, and system feature overload. Interestingly, these factors are a function of the individuals who use the technology, not the technology itself. In this paper, we present the results from three studies that (1) develop and pre-test a scale measurement for technology overload and its distinct dimensions, (2) validate the instrument, and (3) explore the relationship between technology overload and knowledge worker productivity. Our findings demonstrate the relationship between information technology usage and knowledge worker productivity, and they suggest how tradeoffs can be managed to ameliorate technology overload.     

A fuzzy membership filtering aided neural network based transmission loss allocation scheme using game theory

The present paper proposes development of a transmission loss allocation scheme in a deregulated environment using fuzzy memberships and supervised neural networks. This method can be effectively utilized in online applications where game theory based solutions, which otherwise produce acceptable results, cannot be utilized for prohibitive computation load. We propose a fuzzy membership based approach to filter data from a global database and create a local relevant database, for each transaction detail online, each time. A neural network is trained for each such local database formed and utilized for estimating loss allocations among players, for the transaction detail under consideration. The proposed method has been employed for an IEEE 14 bus system and the results of our proposed method have been shown to be sufficiently accurate, when compared to results obtained by using game theoretic approach.     

A decision approach with multiple interactive qualitative objectives for product conceptual schemes based on noncooperative-cooperative game theory

Product development based on a morphological matrix involves the process of decision-based design. Although the decision process can generate conceptual schemes under the guidance of qualitative decision objectives, analysis of the interactions among the qualitative objectives is seldom considered, which can lead to unreliable optimal solutions by combining conflicting principle solutions. In addition, due to the ambiguity of the constraints among the qualitative objectives, multiple feasible schemes with equilibrium states are not considered in the concept decision stage. To solve these problems, a decision approach with multiple interactive qualitative objectives is developed for conceptual schemes based on noncooperative-cooperative game theory to consider the tradeoffs among objectives (e.g., cost, quality and operability) using discrete principle solution evaluation data. First, the morphological analysis method can obtain feasible schemes and determine the principle solutions for each subfunction. Second, the principle solutions are quantified using linguistic terms. Then, the subfunctions are categorized through cluster analysis to determine the suitable principle solution. Third, based on the clustering results, a noncooperative game decision model is constructed to identify multiple Nash equilibrium solutions that satisfy the constraints among the objectives. Fourth, a cooperative game decision model is constructed to obtain the optimal scheme as screened by the noncooperative game model. The case study proves that this approach can choose a relatively superior scheme under the existing technical conditions, thereby preventing inconsistency with the actual design expectations.     

Good programming in transactional memory : Game theory meets multicore architecture

In a multicore transactional memory (TM) system, concurrent execution threads interact and interfere with each other through shared memory. The less interference a thread provokes the better for the system. However, as a programmer is primarily interested in optimizing her individual code’s performance rather than the system’s overall performance, she does not have a natural incentive to provoke as little interference as possible. Hence, a TM system must be designed compatible with good programming incentives (GPI), i.e., writing efficient code for the overall system should coincide with writing code that optimizes an individual thread’s performance. We show that with most contention managers (CM) proposed in the literature so far, TM systems are not GPI compatible. We provide a generic framework for CMs that base their decisions on priorities and explain how to modify Timestamp-like CMs so as to feature GPI compatibility. In general, however, priority-based conflict resolution policies are prone to be exploited by selfish programmers. In contrast, a simple non-priority-based manager that resolves conflicts at random is GPI compatible.     

Solution of multiobjective optimization problems: coevolutionary algorithm based on evolutionary game theory

When attempting to solve multiobjective optimization problems (MOPs) using evolutionary algorithms, the Pareto genetic algorithm (GA) has now become a standard of sorts. After its introduction, this approach was further developed and led to many applications. All of these approaches are based on Pareto ranking and use the fitness sharing function to keep diversity. On the other hand, the scheme for solving MOPs presented by Nash introduced the notion of Nash equilibrium and aimed at solving MOPs that originated from evolutionary game theory and economics. Since the concept of Nash Equilibrium was introduced, game theorists have attempted to formalize aspects of the evolutionary equilibrium. Nash genetic algorithm (Nash GA) is the idea to bring together genetic algorithms and Nash strategy. The aim of this algorithm is to find the Nash equilibrium through the genetic process. Another central achievement of evolutionary game theory is the introduction of a method by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an evolutionary stable strategy (ESS). In this article, we find the ESS as a solution of MOPs using a coevolutionary algorithm based on evolutionary game theory. By applying newly designed coevolutionary algorithms to several MOPs, we can confirm that evolutionary game theory can be embodied by the coevolutionary algorithm and this coevolutionary algorithm can find optimal equilibrium points as solutions for an MOP. We also show the optimization performance of the co-evolutionary algorithm based on evolutionary game theory by applying this model to several MOPs and comparing the solutions with those of previous evolutionary optimization models. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

A decision framework for the analysis of green supply chain contracts: An evolutionary game approach

The overwhelming growth in the struggle between economy and ecology has led managerial research to promote methodologies to reap profits ensuring sustainability of the ecosystem thus venturing into the novel domain incorporating sustainability with supply chain management and coordination. In this paper, we look for a synergetic alliance between the environmental and commercial benefits by establishing coordination between the producer and the retailer to adjudicate their strategies to trigger green practices with the focus on maximizing economic profits by leveraging upon the product’s greenness. A two player game has been developed to stand synonymous to the situation considered. Special considerations have been devised to augment the analysis with the consumer psychology towards the green products to establish a practical outlook involving the greenness and economic returns. Evolutionary dynamics has been adopted extensively to locate the optimal and the most stable point offering the best economic gains. The economically suitable solutions are then substantiated on an existing sustainability metric and hence we aim to offer a holistic view into supply chain dynamics from the perspective of environmental management. The insights drawn provide managerial implications and advantages of having environment as a key focus along with business motives.