Quick and easy game bot detection based on action time interval estimation

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.     

基于终端通信特征的P2P僵尸主机检测

详细分析了P2P僵尸网络的生命周期以及网络特征,从P2P软件和P2P僵尸病毒的网络行为相异性出发,提取其特征向量,并结合三种著名的数据挖掘算法,提出一种基于终端网络行为特征的P2P僵尸主机检测模型——Bot_Founder,并论述了虚拟机环境搭建和实验结果分析.实验结果表明,该模型能高效准确地区分出正常的P2P进程与P2P僵尸进程,检测出处于潜伏阶段的僵尸主机,具有较低的漏判率.     

Do people play MMORPGs for extrinsic or intrinsic rewards?

Several frameworks have been proposed to explain the motivations for playing Massively Multiplayer Online Role-Playing Games (MMORPG). However, no unified view as to whether people play MMORPGs for extrinsic or intrinsic rewards has been proposed in the existing literature. Thus, this study drew upon flow theory, Veblen’s Leisure Class theory, and Yee’s online gaming motivations framework to investigate the effects of extrinsic and intrinsic motivations on the gamer loyalty and gaming behaviors of Turkish MMORPG players. A path model was proposed to articulate the aforementioned relationships. The model showed that gamer loyalty could be predicted by status seeking (extrinsic motivation) and two facets of flow (merging of actions and awareness, and autotelic experience; intrinsic motivation). Furthermore, the game levels reached while playing MMORPGs were affected both by status seeking (extrinsic motivation) and high control of the game (intrinsic motivation). The results also demonstrated that participants with higher social motivation spent more of their daily time on MMORPGs, and players who reported higher levels of autotelic experience spent more money on MMORPGs.     

User cooperation to mitigate the jamming in OFDMA networks

In this paper, jamming and anti-jamming in a time slotted orthogonal frequency division multiple access (OFDMA) are investigated. The users of the network cooperate to mitigate the jamming effect. Interactions among the network users and the jammer are modeled by a mixed integer non-cooperative generalized game which is a NP-hard problem. To relax the problem, a new algorithm is proposed based on channel state information (CSI) to determine the transmission channels and the partners of users in the network. To solve the proposed game, an optimization problem is solved for each player considering the best response of the other player. Another generalized game is proposed to model the cooperation among users of the network. The latter game is transformed to an equivalent Quasi Variational Inequality (QVI). Then, a new gradient based algorithm is proposed by utilizing the Karush-Kuhn-Tucker (KKT) conditions to find the solution of the equivalent QVI. Then, solving the latter game and substituting the best related response, the solution of the former generalized game is also obtained. The convergence of the proposed algorithms, and the uniqueness related to the solution of the both generalized games are proven. Finally, the simulation results confirm the effectiveness of the proposed approach.

     

Causes of State Hostility and Enjoyment in Player Versus Player and Player Versus Environment Video Games

Video games have been under scholarly study since the 1980s, but few studies have blended the research areas of video games and aggression and video game enjoyment. This article seeks to determine how certain game play factors affect hostility, enjoyment, and the relationship between the 2 variables. A total of 289 students played games in player‐versus‐player (PvP) and player‐versus‐environment (PvE) situations. Findings indicate that outcome, competitive situation, and genre interact in their influence on state hostility and enjoyment. Results suggest that in some cases, game players are able to enjoy games despite negative experiences, such as losing, but players in PvP situations experience enjoyment‐reducing levels of hostility, contrary to P. Vorderer et al.'s (2004) complex entertainment model.     

An Efficient Gaming User Oriented Load Balancing Scheme for MMORPGs

Massively multi-player online role-playing games (MMORPGs) follows a client-server model that has the numerous gaming users with many interactions at the same virtual world, massive loading that result in delays, resource shortages, and other such problems occur. Also, faced with high resource demand variability and with misfit resource renting policies, the current practices is to overprovision for each game tens of owned data. Existing load balancing schemes for distributed virtual environments and multiplayer games try to balance the workload among servers by transferring some workload of an overloaded server to other servers. While load balancing algorithms can minimize the average response time of the system, they may also result in frequent client migrations, which may damage the interactivity of an online games. To solve this, many developers devote research to load-balancing servers, yet due to steady and dynamic map divisions, such research is unreliable. Many developers propose algorithms to distribute the load on the server nodes, but the load is usually defined as the number of players on each server, what is not an ideal results. So, we propose a gaming user-oriented load balancing scheme for the load balancing of MMORPGs servers in this paper. This scheme shows effectiveness at dealing with hot-spots and other gatherings of gaming users at specific servers compared to previous methods.     

融媒体时代——乙女游戏中的女性主义和沉浸式传播研究

市场中针对女性用户玩家的游戏都有其特殊性,在视觉和听觉等直接感官方面、游戏中NPC的设置方面、游戏的情节方面以及主题游戏里面所嵌套的小游戏方面,都与一般性的手机游戏有所不同。自2017年《恋与制作人》为代表的乙女游戏风靡中国成为现象级事件之后,女性向手游不断发展——数量大幅度提升,种类多样化,游戏产品的设计流程也逐渐完善,但总体来说在理论研究方面,无论是针对此现象级事件背后所蕴含的社会因素的研究还是针对女性玩家情感心理活动的研究都相对较少,多数研究还是从游戏产业发展的角度出发,因此旨在从女性主义发展的角度出发,去研究针对女性用户玩家的游戏中所蕴含的文化修辞。除此之外,以人为核心的“沉浸式传播”贯穿生活中的方方面面,在女性手游中也将其充分体现。传播实践中,“沉浸”与“场景”常常相辅相成,在乙女游戏中,通过不同游戏场景、游戏人物的设置,玩家在玩游戏的过程中也在有意识或者无意识地接受着游戏中传递出来的价值观念和文化内容。     

Cloud‐Based Gaming Service Platform Supporting Multiple Devices

To implement a cloud game service platform supporting multiple users and devices based on real‐time streaming, there are many technical needs, including game screen and sound capturing, audio/video encoding in real time created by a high‐performance server‐generated game screen, and real‐time streaming to client devices, such as low‐cost PCs, smart devices, and set‐top boxes. We therefore present a game service platform for the running and management of the game screen, as well as running the sound on the server, in which the captured and encoded game screen and sound separately provide client devices through real‐time streaming. The proposed platform offers Web‐based services that allow game play on smaller end devices without requiring the games to be installed locally.     

基于LLMNR协议与证据理论的本地网络CC信息分享机制

僵尸主机(Bot)安全隐蔽地获取控制命令信息是保证僵尸网络能够正常工作的前提。该文针对本地网络同类型Bot隐蔽地获取控制命令信息问题,提出一种基于LLMNR协议与证据理论的命令控制信息分享机制,首先定义了开机时间比和CPU利用率两个评价Bot性能的指标。其次本地网络中多个同类Bot间利用LLMNR Query包通告各自两个指标值,并利用D-S证据理论选举出僵尸主机临时代表BTL(Bot Temporary Leader)。接着仅允许BTL与命令控制服务器进行通信并获取命令控制信息。最后,BTL通过LLMNR Query包将命令控制信息分发给其它Bot。实验结果表明,该机制能使多个同类Bot完成命令控制信息的共享,选举算法能根据Bot评价指标实时有效选举出BTL,在网络流量较大时仍呈现较强的鲁棒性,且选举过程产生流量也具有较好隐蔽性。     

一种基于多示例学习的运动员检测方法

体育视频包含大量不同类型的人体,其中运动员的行为与比赛进程和视频内容直接相关,因此运动员检 测是体育视频分析的关键环节。现有人体目标检测算法在通用人体检测任务上取得了良好的性能,但是无法有效区分运动员和非运动员。专门训练一个运动员检测模型需要标注大量的运动员位置,成本较高。本文提出了一种基于多示例学习的人体目标检测方法。在通用人体检测的基础上,引入多示例学习模块,基于图像级标注,通过弱监督方式自动学习获取特征映射矩阵,将人体特征映射到运动员特征空间,最后通过度量人体特征与运动员特征之间的相似度,实现运动员与非运动员的区分。对比实验结果表明,本文方法充分利用通用人体检测框架,以 极小的标注数据量达到了专门训练运动员检测模型的精度。      

Improving evidence-based assessment of players using serious games

Serious games are highly interactive systems which can therefore capture large amounts of player interaction data. This data can be analyzed to provide a deep insight into the effect of the game on its players. However, traditional techniques to assess players of serious games make little use of interaction data, relying instead on costly external questionnaires. We propose an evidence-based process to improve the assessment of players by using their interaction data. The process first combines player interaction data and traditional questionnaires to derive and refine game learning analytics variables, which can then be used to predict the effects of the game on its players. Once the game is validated, and suitable prediction models have been built, the prediction models can be used in large-scale deployments to assess players solely based on their interactions, without the need for external questionnaires. We briefly describe two case studies where this combination of traditional questionnaires and data mining techniques has been successfully applied. The evidence-based assessment process proposed radically simplifies the deployment and application of serious games in real class settings.     

A Game Theoretical Framework for Dynamic Pricing-Based Routing in Self-Organized MANETs

In self-organized mobile ad hoc networks (MANETs) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other or honestly revealing its private information, cannot be directly assumed. The pricing mechanism is one way to provide incentives for the users to act cooperatively by awarding some payment for cooperative behaviors. In this paper, we consider efficient routing in self-organized MANETs and model it as multi-stage dynamic pricing games. A game theoretical framework for dynamic pricing-based routing in MANETs is proposed to maximize the sender/receiver?s payoff by considering the dynamic nature of MANETs. Meanwhile, the forwarding incentives of the relay nodes can also be maintained by optimally pricing their packet-forwarding services based on the auction rules and introducing the Cartel Maintenance enforcing mechanism. The simulation results illustrate that the proposed dynamic pricing-based routing approach has significant performance gains over the existing static pricing approaches.     

An evolutionary bargaining‐based approach for incentivized cooperation in opportunistic networks

Opportunistic networking enables users to communicate in an environment where connectivity is intermittent or unstable. However, such networking scheme assumes that mobile nodes voluntary cooperate, which cannot be guaranteed. Some nodes can simply exhibit selfish behavior and thus diminish the effectiveness of the approach. In this paper, a game scenario is formulated in which the nodes try to convince each other to participate in packets forwarding. Each node is considered as a player in this game. When a node comes in the communication range of another, a bargaining game starts between them as part of the message forwarding process. Both players try to have a mutual agreement on a price for message forwarding. We present a new incentive mechanism called evolutionary bargaining‐based incentive scheme (EBIS) to motivate selfish nodes to cooperate in data forwarding. In EBIS, a node negotiates with other nodes to obtain an agreeable amount of credit for its forwarding service. Nodes apply a sequential bargaining game and then adapt their strategies using an evolutionary model to maximize the probability of reaching an agreement. Unlike classical bargaining games, nodes in our model are boundedly rational. In addition, we use the evolutionary stable strategy (ESS) concept to determine the adaptive strategies for the nodes. The comparison of EBIS with a benchmarked model demonstrates that EBIS performs better in terms of packet delivery ratio and average latency.     

Adaptive traffic sampling for P2P botnet detection

Peer‐to‐peer (P2P) botnets have become one of the major threats to network security. Most existing botnet detection systems detect bots by examining network traffic. Unfortunately, the traffic volumes typical of current high‐speed Internet Service Provider and enterprise networks are challenging for these network‐based systems, which perform computationally complex analyses. In this paper, we propose an adaptive traffic sampling system that aims to effectively reduce the volume of traffic that P2P botnet detectors need to process while not degrading their detection accuracy. Our system first identifies a small number of potential P2P bots in high‐speed networks as soon as possible, and then samples as many botnet‐related packets as possible with a predefined target sampling rate. The sampled traffic then can be delivered to fine‐grained detectors for further in‐depth analysis. We evaluate our system using traffic datasets of real‐world and popular P2P botnets. The experiments demonstrate that our system can identify potential P2P bots quickly and accurately with few false positives and greatly increase the proportion of botnet‐related packets in the sampled packets while maintain the high detection accuracy of the fine‐grained detectors.     

A coalitional game‐based relay load balancing and power allocation scheme in decode‐and‐forward cellular relay networks

In this paper, a game theoretic relay load balancing and power allocation scheme is proposed for downlink transmission in a decode‐and‐forward orthogonal frequency division multiple access‐based cellular relay network. A system with a base station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it may happen that some relays have more users connected to them than other relays, which results in having unbalanced load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the proposed game‐based scheme can considerably improve the average sum‐spectral efficiency. Moreover, it shows that by applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays. Copyright © 2015 John Wiley & Sons, Ltd.     

A routing protocol for urban vehicular ad hoc networks to support non-safety applications

The Urban Vehicular Ad Hoc Network (UVANET) provides non-safety applications like media sharing, internet service, file transfer, gaming, and so on. Routing plays an important role in providing better services to the users in UVANET. In this paper, a novel routing protocol is proposed for UVANET to support non-safety applications. We consider a non-safety application wherein the drivers and passengers of different parking lots can play multi-player games with each other. To play the games smoothly, the game data should reach the destination in a minimum time. Simulation results show that when the vehicle density in the city area is high, the proposed protocol meets the end-to-end delay tolerance of 100 ms. Finally, an experiment is performed to validate the proposed routing protocol by running a simple puzzle game in a UVANET prototype designed in an indoor laboratory environment and in an outdoor environment.     

认知无线电中基于无限次重复博弈的功率控制算法

通过博弈来实现认知无线电中的功率控制。当两用户功率控制博弈时,用户通过功率的迭代注水法实现最大化自身速率,达到纳什均衡。功率分配的纳什均衡点构成囚徒困境,但这种囚徒困境的均衡点并非全局最优。应用两用户功率控制的无限次重复博弈算法,通过选择严厉的触发策略,当折扣因子σ足够接近于1,使两用户在无限次重复博弈中一直合作使囚徒走出了困境,最终达到了功率分配的帕雷托最优的均衡结果。     

Hybrid model of inter-stage spectrum trading in multistage game-theoretic framework

Dynamic spectrum access(DSA),consisting of spectrum sharing and spectrum trading stage,becomes a promising approach to increase the efficiency of spectrum usage and system performance.In this paper,from the perspective of individual interest optimization,we focus on strategy adaptation of network users and their interaction in spectrum trading process.Considering adverse effects on decision-making accuracy and the fairness among network users via local information acquirement,a hybrid game model based on global information of relevant spectrum is proposed to formulate intelligent behaviors of both primary and secondary users.Specifically,by using the evolutionary game theory,a spectrum-selection approach for the evolution process of secondary users is designed to converge to the evolutionary equilibrium gradually.Moreover,competition among primary users is modeled as a non-cooperative game and an iterative algorithm is employed to achieve the Nash equilibrium.The simulation results show that the proposed hybrid game model investigates network dynamics under different network parameter settings.     

Coalitional game‐based behavior analysis for spectrum access in cognitive radios

The core of cognitive radio paradigm is to introduce cognitive devices able to opportunistically access the licensed radio bands. The coexistence of licensed and unlicensed users prescribes an effective spectrum hole‐detection and a non‐interfering sharing of those frequencies. Collaborative resource allocation and spectrum information exchange are required but often costly in terms of energy and delay. In this paper, each secondary user (SU) can achieve spectrum sensing and data transmission through a coalitional game‐based mechanism. SUs are called upon to report their sensing results to the elected coalition head, which properly decides on the channel state and the transmitter in each time slot according to a proposed algorithm. The goal of this paper is to provide a more holistic view on the spectrum and enhance the cognitive system performance through SUs behavior analysis. We formulate the problem as a coalitional game in partition form with non‐transferable utility, and we investigate on the impact of both coalition formation and the combining reports costs. We discuss the Nash Equilibrium solution for our coalitional game and propose a distributed strategic learning algorithm to illustrate a concrete case of coalition formation and the SUs competitive and cooperative behaviors inter‐coalitions and intra‐coalitions. We show through simulations that cognitive network performances, the energy consumption and transmission delay, improve evidently with the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.