经济代写|博弈论代写Game Theory代考|ECOS3012

如果你也在 怎样代写博弈论Game Theory这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。

博弈论是对理性主体之间战略互动的数学模型的研究。它在社会科学的所有领域,以及逻辑学、系统科学和计算机科学中都有应用。最初,它针对的是两人的零和博弈,其中每个参与者的收益或损失都与其他参与者的收益或损失完全平衡。在21世纪,博弈论适用于广泛的行为关系;它现在是人类、动物以及计算机的逻辑决策科学的一个总称。

statistics-lab™ 为您的留学生涯保驾护航 在代写博弈论Game Theory方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写博弈论Game Theory代写方面经验极为丰富,各种代写博弈论Game Theory相关的作业也就用不着说。

我们提供的博弈论Game Theory及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
经济代写|博弈论代写Game Theory代考|ECOS3012

经济代写|博弈论代写Game Theory代考|Artificial Intelligence and Cybersecurity

Artificial intelligence (AI) refers to a set of computing technologies that are designed to accomplish tasks that require intelligence, which can be broadly defined as interpreting observations of the environment, using knowledge and problem solving to select actions to efficiently accomplish goals, and adapting to new information and situations as they arise. AI encompasses a large

number of subdisciplines including knowledge representation, reasoning and decision-making, different types of learning, language processing and vision, robotics, and many others. While general-purpose “human level” AI has not yet been achieved and is not likely to emerge in the near future, AI technologies are rapidly improving in many areas and are being used in many new applications with increasing complexity and impact on our everyday lives. For example, AI agents have already met or exceeded the performance of humans in complex games of strategy such as Go (Singh et al. 2017) and Poker (Brown and Sandholm 2019), and they are also achieving success in many more general tasks such as medical diagnostics (Ker et al. 2017) and self-driving vehicles (Badue et al. 2019), to name just a few examples. For specific, well-defined domains and tasks, it is typical for AI systems to already be on par with human performance, and in many cases, A] systems excel at tasks in which humans perform very poorly. Given the rapid advances in AI, there is a great opportunity to deploy AI methods to make progress on the most challenging problems we face as a society.

Cybersecurity represents one of the great emerging challenges of the twenty-first century. This is driven fundamentally by the rapid development and adoption of computing and networking technologies throughout all areas of society. While providing dramatic benefits and fundamentally changing many areas of business, government, media, military, and personal life, the general paradigm has been to push out new technology and features as quickly as possible without great diligence given to security and privacy implications. The complexity of the interactions of the systems combined with the human users and administrators that interact with them has led to the current situation in which cyber attacks are a constant and very costly threat (Lewis 2018).

As we rely ever more on computing systems to perform critical functions (e.g., controlling the electric grid, driving a vehicle, or making medical diagnoses), the importance of addressing the cybersecurity problem becomes paramount. In addition, future conflicts among nation states as well as terrorist actors will involve attacks and defense in cyberspace as major elements. Those groups with the most robust and resilient computing infrastructure will have a significant security advantage in other areas as well.

AI is already used in many areas of cybersecurity, and new applications of AI will be a key part of the solution for improving cybersecurity in the future. Users, software developers, network administrators, and cyber response teams all have a limited ability to respond to cyber threats, and they need better automated tools for configuration, threat detection and evaluation, risk assessment, automated response, etc., to improve accuracy and reduce the costs of providing security. AI is especially important in cyberspace, because many of the events and responses in security situations take place either at speeds that are much too fast for an effective human response or at scales that are much too large for humans to process effectively. However, the many unique aspects of the cybersecurity domain present novel challenges for developing and deploying AI solutions. This book provides a broad view of recent progress in AI related to cybersecurity, including deep dives into many specific techniques and applications, as well as discussions of ongoing challenges and future work.

经济代写|博弈论代写Game Theory代考|Game Theory for Cybersecurity

Many problems in cybersecurity are fundamentally decision-making problems under complex, uncertain, multi-agent conditions. Game theory provides a foundational set of mathematical tools for modeling these types of decision problems that can allow us to make better decisions (both as

humans and automated systems), as well as understand the reasons for these decisions and what assumptions and data they depend on. While many problems in cybersecurity can be modeled as (approximately) zero-sum games between a defender and an attacker, there are also more complex games that are not zero-sum or that involve relationships among multiple attackers and defenders (Alpcan and Başar 2010; Manshaei et al. 2013; Pawlick and Zhu 2021). Game theory is not a single idea or approach, but a very diverse collection of modeling techniques and solution concepts and algorithms that can be applied to many different situations. Therefore, it is not a simple “off the shelf” technology that can be easily adapted to any problem. Rather, it is a powerful set of techniques that requires a clear understanding of the problem being modeled, as well as the strengths and limitations of different solution techniques to arrive at solutions that can be highly effective in practice.

In this book, we start with a general overview of basic game theory concepts and then cover a variety of specific modeling and solution techniques, as well as applications of these techniques in cybersecurity applications. These examples are intended to provide a good representation of common approaches in this very active research area, where new problems are being solved and new approaches are being developed at a rapid pace.

经济代写|博弈论代写Game Theory代考|Machine Learning for Cybersecurity

Another fundamental problem in cybersecurity is using data to make predictions, identify patterns, perform classification, or adapt strategies over time. All of these tasks fall under the general domain of machine learning, which encompasses different paradigms including supervised, unsupervised, and reinforcement learning. Machine learning is a core discipline within AI that studies how agents can use historical data to adapt and improve their performance on future tasks. This area of AI has seen especially dramatic progress and success in certain tasks in recent years, especially with the advancement of “deep learning” approaches that focus on using the data to find sophisticated internal representations of features, rather than having a human specify features to the algorithm. Deep learning techniques have been very successful in many areas including specific cybersecurity tasks such as intrusion detection and malware analysis (Xin et al. 2018; Berman et al. 2019). However, deep learning does typically require large data sets and lots of computing resources to achieve good results, so it is not the best solutions for all problems.

In this book, we cover a range of topics in machine learning for cybersecurity, including examples of specific applications, theoretical and empirical evaluations of new techniques, the connections between machine learning and game theory in multi-agent settings, and analysis of some of the problems that can arise when applying machine learning in an adversarial context. Here, we briefly overview some of the main challenges for applying machine learning to cybersecurity problems that are addressed throughout this book:

  1. Problem definition and data sets. Similar to the game theory approaches, machine learning approaches must also define exactly what the problem is and what data can be used to learn the desired model. First, one faces the problems of specifying exactly what you are trying to predict, how this will be useful to the decision-maker, and how you will characterize uncertainty about the predictions. Next, you need to be able to identify data that will actually be useful for making these predictions; usually this needs to be data in sufficient quantity and without too much noise or data quality issues to be useful. In addition, it may be necessary to label the data based on human inputs, which can be expensive. In cybersecurity, there is the additional challenge that high-quality data sets are hard to acquire due to privacy and security concerns, and it is also hard to get direct data about attacker activities. All of this means that the first problem that must be solved before machine learning can be useful is to identify good data sources.
经济代写|博弈论代写Game Theory代考|ECOS3012

博弈论代考

经济代写|博弈论代写Game Theory代考|Artificial Intelligence and Cybersecurity

人工智能(Artificial Intelligence,AI)是指旨在完成需要智能的任务的一组计算技术,可以广义地定义为解释对环境的观察,利用知识和解决问题的方法来选择行动以有效地实现目标,并适应出现新的信息和情况。人工智能涵盖了一个大

子学科的数量,包括知识表示、推理和决策、不同类型的学习、语言处理和视觉、机器人技术等等。虽然通用的“人类水平”人工智能尚未实现,也不太可能在不久的将来出现,但人工智能技术在许多领域正在迅速改进,并被用于许多新应用中,其复杂性和对我们日常生活的影响越来越大. 例如,人工智能代理在围棋(Singh et al. 2017)和扑克(Brown and Sandholm 2019)等复杂策略游戏中的表现已经达到或超过了人类,并且它们还在许多更一般的任务中取得了成功,例如例如医疗诊断(Ker et al. 2017)和自动驾驶汽车(Badue et al. 2019),仅举几个例子。对于特定的、定义明确的领域和任务,人工智能系统通常已经与人类表现相提并论,并且在许多情况下,A] 系统擅长于人类表现非常差的任务。鉴于人工智能的快速发展,有一个很好的机会来部署人工智能方法,以在我们作为一个社会面临的最具挑战性的问题上取得进展。

网络安全是 21 世纪新出现的重大挑战之一。这从根本上是由计算和网络技术在社会各个领域的快速发展和采用所推动的。虽然提供了巨大的好处并从根本上改变了商业、政府、媒体、军事和个人生活的许多领域,但总体范式一直是尽快推出新技术和新功能,而无需对安全和隐私影响付出很大的努力。系统交互的复杂性与与之交互的人类用户和管理员相结合,导致当前的情况是网络攻击是一种持续存在且代价高昂的威胁(Lewis 2018)。

随着我们越来越依赖计算系统来执行关键功能(例如,控制电网、驾驶车辆或进行医疗诊断),解决网络安全问题变得至关重要。此外,未来民族国家之间以及恐怖分子之间的冲突将涉及网络空间的攻击和防御作为主要因素。那些拥有最强大和最有弹性的计算基础设施的群体在其他领域也将拥有显着的安全优势。

人工智能已经在网络安全的许多领域得到应用,人工智能的新应用将成为未来提高网络安全解决方案的关键部分。用户、软件开发人员、网络管理员和网络响应团队应对网络威胁的能力都有限,他们需要更好的自动化工具进行配置、威胁检测和评估、风险评估、自动化响应等,以提高准确性和降低提供安全的成本。人工智能在网络空间中尤为重要,因为安全局势中的许多事件和响应发生的速度要么太快而无法进行有效的人类响应,要么发生的规模太大而无法有效处理。然而,网络安全领域的许多独特方面为开发和部署人工智能解决方案提出了新的挑战。本书提供了与网络安全相关的人工智能最新进展的广泛观点,包括深入研究许多特定技术和应用,以及对当前挑战和未来工作的讨论。

经济代写|博弈论代写Game Theory代考|Game Theory for Cybersecurity

网络安全中的许多问题从根本上说是复杂、不确定、多主体条件下的决策问题。博弈论提供了一套基本的数学工具来模拟这些类型的决策问题,可以让我们做出更好的决策(两者都是

人类和自动化系统),以及了解这些决策的原因以及它们所依赖的假设和数据。虽然网络安全中的许多问题可以(近似)建模为防御者和攻击者之间的零和博弈,但也有更复杂的博弈不是零和博弈,或者涉及多个攻击者和防御者之间的关系(Alpcan 和 Başar 2010; Manshaei 等人,2013;Pawlick 和 Zhu 2021)。博弈论不是一个单一的想法或方法,而是一个非常多样化的建模技术、解决方案概念和算法的集合,可以应用于许多不同的情况。因此,它不是一种可以轻松适应任何问题的简单“现成”技术。相反,它是一组强大的技术,需要清楚地理解正在建模的问题,

在本书中,我们从基本博弈论概念的一般概述开始,然后介绍各种特定的建模和求解技术,以及这些技术在网络安全应用中的应用。这些示例旨在很好地展示这个非常活跃的研究领域中的常用方法,其中新问题正在得到解决,新方法正在快速开发中。

经济代写|博弈论代写Game Theory代考|Machine Learning for Cybersecurity

网络安全的另一个基本问题是使用数据进行预测、识别模式、执行分类或随着时间的推移调整策略。所有这些任务都属于机器学习的一般领域,其中包括不同的范式,包括监督学习、无监督学习和强化学习。机器学习是人工智能中的一门核心学科,它研究代理如何使用历史数据来适应和提高他们在未来任务中的表现。近年来,人工智能的这一领域在某些任务中取得了特别显着的进步和成功,特别是随着“深度学习”方法的进步,这些方法专注于使用数据来寻找复杂的内部特征表示,而不是让人类指定特征来算法。深度学习技术在许多领域都非常成功,包括特定的网络安全任务,例如入侵检测和恶意软件分析(Xin et al. 2018; Berman et al. 2019)。然而,深度学习通常需要大量的数据集和大量的计算资源才能获得好的结果,因此它并不是所有问题的最佳解决方案。

在本书中,我们涵盖了网络安全机器学习的一系列主题,包括特定应用示例、新技术的理论和实证评估、多智能体设置中机器学习和博弈论之间的联系,以及对一些在对抗环境中应用机器学习时可能出现的问题。在这里,我们简要概述了将机器学习应用于本书所解决的网络安全问题的一些主要挑战:

  1. 问题定义和数据集。与博弈论方法类似,机器学习方法还必须准确定义问题是什么以及可以使用哪些数据来学习所需的模型。首先,人们面临的问题是准确地说明您要预测的内容,这对决策者有何用处,以及您将如何描述预测的不确定性。接下来,您需要能够识别对做出这些预测实际上有用的数据;通常这需要有足够数量的数据,并且没有太多的噪音或数据质量问题才能有用。此外,可能需要根据人工输入标记数据,这可能很昂贵。在网络安全方面,由于隐私和安全问题,难以获得高质量的数据集是一个额外的挑战,而且也很难获得有关攻击者活动的直接数据。所有这些意味着在机器学习有用之前必须解决的第一个问题是识别好的数据源。
经济代写|博弈论代写Game Theory代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构,多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务,包括但不限于Essay代写,Assignment代写,Dissertation代写,Report代写,小组作业代写,Proposal代写,Paper代写,Presentation代写,计算机作业代写,论文修改和润色,网课代做,exam代考等等。写作范围涵盖高中,本科,研究生等海外留学全阶段,辐射金融,经济学,会计学,审计学,管理学等全球99%专业科目。写作团队既有专业英语母语作者,也有海外名校硕博留学生,每位写作老师都拥有过硬的语言能力,专业的学科背景和学术写作经验。我们承诺100%原创,100%专业,100%准时,100%满意。

随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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